Grouting process dynamic control method based on statistical characteristics of water permeability and grouting amount per meter

Abstract

The invention provides a grouting process dynamic control method based on statistical characteristics of a water permeability rate and a grouting amount per meter, which comprises the following steps: collecting water pressure test data (pressure P, flow Q and segment length L) and grouting process data (dry material weight M and segment length L) through a system, and calculating the water permeability rate q and the grouting amount per meter m; sorting and grouping the permeable rate data and the grouting amount data per meter, then carrying out normality test, and respectively establishing a mapping relation table between permeable rate groups and standard deviation values of the grouting amount per meter or percentile values per meter according to test results; in the grouting process, the water permeability is monitored and calculated in real time, the water permeability is matched with the mapping table, and the grout proportion is adjusted in a stepped mode according to the statistical interval where the current grouting amount per meter is located; and after grouting is finished, the grouting effect is evaluated according to the statistical interval where the final grouting amount is located. According to the method, quantitative control over the grouting process is achieved, and the problem that grout is wasted or the diffusion range is insufficient due to the fact that the proportion of the grout is adjusted through experience judgment is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal mine water disaster prevention, in particular to a grouting process dynamic control method based on water permeability and per meter grouting amount statistical characteristics. BACKGROUND

[0002] The coal mine water disaster ground area treatment technology is to construct a directional horizontal branch well in the coal seam floor aquifer on the ground, to probe the water storage space and water channel of the karst, fissure and fault of the aquifer in advance, and to conduct grouting, so that the grout diffuses and solidifies in the rock mass, reduces the permeability of the aquifer, enhances the strength of the aquifer, or changes the aquifer into an aquifuge, to increase the effective thickness of the coal seam floor aquifuge, thereby reducing the floor water inrush threat degree during the mining of the working face. The ground area treatment engineering technology has the advantages of strong applicability, high treatment efficiency, and being not affected by the underground operation space and the number of personnel, etc., and is one of the technologies that have developed rapidly in the treatment of coal mine floor water disaster in recent years, and its application range is also becoming more and more extensive.

[0003] Grouting is a relatively important link in the ground area treatment engineering, and the grouting process control plays a key role in the grouting cost, grouting efficiency and grouting effect, etc. At present, the grouting process adheres to the principle of "thinning first and thickening later, and grouting as much as possible", and adjusts the grouting parameters such as pressure, flow and grout specific gravity to control the grouting process, so as to reach the grouting end standard. Among them, the adjustment of the grout specific gravity mostly depends on the engineer's experience, lacks quantitative basis, and is easy to cause waste of grout (the grout specific gravity is raised too late) or insufficient diffusion range (the grout specific gravity is raised too early), and it is difficult to timely grasp the performance of the segment long grouting amount in the area. SUMMARY

[0004] The present application provides a grouting process dynamic control method based on water permeability and per meter grouting amount statistical characteristics to solve the technical defects of the prior art.

[0005] To solve the above technical problems, the technical scheme of the present application is a grouting process dynamic control method based on water permeability and per meter grouting amount statistical characteristics, comprising the following steps: Step 1, data acquisition and pretreatment, the data acquisition includes water pressure test data and grouting data, the water pressure test data includes pressure, flow and segment length, the grouting data includes grouting dry material weight, and the data pretreatment includes water permeability and per meter grouting amount calculation, the water permeability calculation formula is: q = Q / (P*L) The per meter grouting amount calculation formula is: m = M / L ; Wherein m is the per meter grouting amount, M is the grouting dry material weight, and L is the segment length. Step 2, data sorting and grouping, the calculated water permeability data is sorted from small to large according to the numerical value, and the corresponding per meter grouting amount data is adjusted in position with the water permeability sorting, when grouping, the number of groups is determined according to the total sample quantity, and combined with the actual engineering, when the sample quantity is small, the number of groups is small, and when the sample quantity is large, the number of groups is increased accordingly: Step 3, data normality test and cleaning: Step 4, statistical model construction, for the normal distribution group: calculate the standard deviation deviation value of per meter grouting amount: V k = μ + kσ, wherein V k is the standard deviation deviation value, μ is the mean value of per meter grouting amount of the group, σ is the standard deviation of per meter grouting amount of the group, k is the deviation coefficient, and a standard deviation deviation value mapping table of water permeability-per meter grouting amount is established, and for the non-normal distribution group: calculate a plurality of percentile values of per meter grouting amount, and a percentile value mapping table of water permeability-per meter grouting amount is established, Step 5, dynamic control of grouting process, (1) according to the water pressure test data before grouting, the water permeability q of the current grouting section length is calculated current： (2) matching the mapping table to determine the group and statistical characteristics: (3) real-time calculation of cumulative per meter grouting amount m current , , ΔL is the cumulative length of the current grouting section, and ∑M is the cumulative dry material weight of the current grouting section: (4) control decision, when m current < a certain coefficient of per meter grouting amount mean value, or < a certain percentile value, the grouting pressure and the grouting flow are executed according to the original engineering design, and the slurry specific gravity remains unchanged, when mcurrent≥ the corresponding coefficient of per meter grouting amount mean value, or ≥ the corresponding percentile value, adjustment is executed, (5) grouting end judgment condition: according to the requirements in the original engineering design, Step 6, grouting effect evaluation.

[0006] Specifically, the data collection period in step 1 is in the stable pressure stage, and the pressure value and flow value in the stable pressure stage are used as the basis for calculation, and the dry material weight includes the total weight of cement, clay and fly ash.

[0007] Specifically, in step 2, the grouping adopts K-means clustering algorithm to group the sorted water permeability data, and at least ensures that the sample quantity of each group is not less than 15.

[0008] Specifically, the normality test in step 3 uses Shapiro-Wilk test method to perform normality test on the water permeability data and the grouting amount per meter data in each group respectively, the data cleaning uses Z-score method to perform abnormal value screening and data cleaning on the normal distribution group, the abnormal value elimination condition is that ∣X-μ∣>3σ, wherein X is a data in the group, μ is the mean of the group, and σ is the standard deviation of the group, the box plot method is used to perform abnormal value screening and data cleaning on the non-normal distribution group, the abnormal value elimination condition is that X<Q1-1.5*IQR or X>Q3+1.5*IQR, wherein X is a single data point, Q1 is the first quartile, Q3 is the third quartile, and IQR is the interquartile range.

[0009] Specifically, in step 4, the k is included in {-1.75, -1.25, -0.75, -0.5, -0.25, 0, +0.25, +0.5, +0.75, +1.25, +1.75}, and the percentile value includes P5, P10, P20, P30, P40, P50, P60, P70, P80, P90 and P95.

[0010] Specifically, in step 5, when m current <The certain coefficient in the average grouting amount per meter is 0, the certain percentile value is P50, and the adjustment is performed as shown in Table 1, Table 1 Grouting slurry specific gravity adjustment table according to grouting amount per meter

[0011] Specifically, in step 6, the grouting effect evaluation evaluation standard is shown in Table 2, Table 2 Grouting effect evaluation table according to grouting amount per meter distribution characteristics

[0012] For the area with less grouting amount, the target value is recalculated by switching to a higher level of water permeability group or a backup grouting hole is enabled to form a surrounding circle, and for the area with more grouting amount, the grouting is immediately stopped to check the pipeline sealing or to replace the double liquid slurry to accelerate solidification or to reduce the grouting pressure level.

[0013] The present application realizes dynamic monitoring of the grouting amount per meter and real-time adjustment of the grouting slurry specific gravity by establishing the mapping relationship between the water permeability and the grouting amount per meter, solves the problem that the adjustment of the grouting slurry specific gravity in the grouting process depends on the field experience and lacks quantitative basis and real-time performance, guarantees the grouting effect, reduces the grouting cost, and has great safety and economic significance. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings of the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only relate to some of the embodiments of the present application and are not a limitation on the present application.

[0015] Figure 1 A general flowchart of a grouting process dynamic control method based on water permeability and statistical characteristics of grouting amount per meter according to the present application; Figure 2 A dynamic control flowchart of a grouting process dynamic control method based on water permeability and statistical characteristics of grouting amount per meter according to the present application. DETAILED DESCRIPTION

[0016] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application but not all the embodiments of the present application. Based on the described embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0017] Unless otherwise defined, the technical terms or scientific terms used herein should be understood as their common meanings to those of ordinary skill in the art to which the present application belongs.

[0018] As shown in Figure 1 and Figure 2 , the technical solution of the present application is a grouting process dynamic control method based on water permeability and statistical characteristics of grouting amount per meter, which comprises the following steps: Step 1, data acquisition and preprocessing, the data acquisition comprises water pressure test data and grouting data, the water pressure test data comprises pressure, flow rate and segment length, the grouting data comprises grouting dry weight, the data preprocessing comprises water permeability and grouting amount per meter calculation, the water permeability calculation formula is: ; wherein q is water permeability, Q is flow rate, P is pressure, and L is segment length The grouting amount per meter calculation formula is: ; ; wherein m is grouting amount per meter, M is grouting dry weight, and L is segment length Step 2, data sorting and grouping, the calculated water permeability data is sorted from small to large according to the numerical value, the corresponding grouting amount per meter data is adjusted in position synchronously with the water permeability sorting, the grouping number is determined according to the total sample quantity and combined with the engineering practice, when the sample quantity is small, the grouping number is small, and when the sample quantity is large, the grouping number is increased accordingly: Step 3, data normality test and cleaning: Step 4, statistical model construction, for the normal distribution group: calculate several standard deviation deviation values of the grouting amount per meter: V k = μ + kσ, wherein V k is the standard deviation deviation value, μ is the average grouting amount per meter of the group, σ is the standard deviation of the grouting amount per meter of the group, k is the deviation coefficient, a standard deviation deviation value-grouting rate per meter mapping table is established, and for the non-normal distribution group: calculate several percentile values of the grouting amount per meter, and a percentile value-grouting rate per meter mapping table is established: Step 5, dynamic control of the grouting process, (1) According to the water pressure test data before grouting, the water permeability q of the current grouting section length is calculated current， (2) Match the mapping table to determine the group and statistical characteristics, (3) Real-time calculation of the cumulative grouting amount per meter m current , ΔL is the cumulative length of the current grouting section, and ∑M is the cumulative dry material weight of the current grouting section, (4) Control decision, when m current <the coefficient of the average grouting amount per meter, or <a certain percentile value, the grouting pressure and the grouting flow are executed according to the original engineering design, and the slurry specific gravity remains unchanged, when mcurrent≥ the corresponding coefficient of the average grouting amount per meter, or ≥ the corresponding percentile value, adjustment is executed, (5) Grouting end judgment condition: execute according to the requirements in the original engineering design, Step 6, grouting effect evaluation.

[0019] Data collection and preprocessing is the basic link of the present application, and the data must be accurate, true and real. The water pressure test data collection includes collecting pressure (P, unit: MPa), flow (Q, unit: L / min) and section length (L, unit: m), for example: pressure 2.5 MPa, flow 15.3 L / min, section length 45 m, grouting data collection corresponds to dry material weight (M, unit: t) and section length (L, unit: m) of the grouting section, and it is ensured that the grouting section length is completely consistent with the water pressure test section. For example: dry material weight 32.4 t, section length 45 m. The water permeability and the grouting amount per meter are calculated, and the water permeability is calculated according to the following formula, Taking example data as an example: q = 15.3 / (2.5x45) = 0.136 (Lu). The grouting amount per meter is calculated according to the following formula, ​For example, m = 32.4 / 45 = 0.72 (t / m). Data sorting and grouping sort the calculated water permeability data from small to large, and the corresponding per-meter grouting amount data is adjusted in position with the water permeability sorting. The number of groups is determined according to the total number of samples and combined with the actual engineering. In the case of a certain total number of samples, the more the number of groups, the more targeted the application in the later stage, but it will lead to a smaller number of samples in each group, and the applicability of statistical characteristics will be weaker. When the number of samples is small, the number of groups should be small, and when the number of samples is large, the number of groups can be correspondingly large. Data normality test is to distinguish between cases. It is divided into normal distribution group (both variables are normally distributed) and non-normal distribution group (single variable is non-normal or both variables are non-normal). Different characteristics of data are treated differently. Cleaning is to clean some non-representative data to improve the quality of subsequent time processing. For the normal distribution group: calculate the standard deviation deviation value of per-meter grouting amount, and establish a standard deviation deviation value mapping table of water permeability-per-meter grouting amount. For the non-normal distribution group: calculate the percentile value of per-meter grouting amount, and establish a percentile value mapping table of water permeability-per-meter grouting amount to provide a table lookup basis for subsequent operations. The mapping table adopts a two-dimensional classification system: the vertical dimension: the formation is divided into multiple continuous intervals according to the size of the water permeability, forming a hierarchical framework, and the horizontal dimension: binding the corresponding grouting amount statistical characteristics (standard deviation deviation value + percentile value) for each water permeability interval, constructing a two-way mapping table. The vertical water permeability classification represents different geological conditions, which directly affects the core parameter of grouting demand. The horizontal coefficient lists a series of values representing the standard deviation multiple deviation from the mean value of each water permeability group. These coefficients are used to quantify the fluctuation range of grouting amount, but the benchmark is always anchored to the mean value of the corresponding water permeability group. The dynamic control basis of the grouting process is the above-mentioned two-way mapping table. This table model can be used as a standardized operation guide to determine the grouting amount. Its core logic is "using historical data as a baseline and dynamically adjusting according to the current geological conditions". First, determine the water permeability and locate the group, then select the target deviation coefficient, then calculate the target grouting amount using the formula: target grouting amount = mean value (μ) of the group + coefficient x standard deviation (σ) of the group. This "table lookup and dynamic correction" mode ensures construction efficiency and avoids blindness through the constraint of historical data, which is an economic and effective solution to achieve "precise grouting". Finally, dynamic execution and feedback correction. Grouting effect evaluation is the last step. The areas with less grouting amount and more grouting amount in the abnormal area should be the key areas for grouting effect test.

[0020] Specifically, the data collection period in step 1 is in the stable pressure stage, and the pressure value and flow value in the stable pressure stage are representative as the basis for calculation. The dry material weight includes the total weight of cement, clay and fly ash, which are common fillers.

[0021] Specifically, the grouping in step 2 groups the sorted water permeability data using the K-means clustering algorithm, ensuring that the sample size of each group is at least 15. The K-means clustering algorithm is logically clear and intuitive, and its core idea is to divide the data into K clusters to minimize the distance between data points within the cluster and maximize the distance between clusters. This process is completed through iterative adjustment of the centroid, and the steps are clear and easy to understand. Ensuring that the sample size of each group is at least 15 guarantees the representativeness of the samples.

[0022] Specifically, the normality test in step 3 uses the Shapiro-Wilk test method to perform normality tests on the water permeability data and the grouting amount per meter data in each group, respectively. The data cleaning uses the Z-score method to perform outlier screening and data cleaning for normally distributed groups. The outlier removal condition is ∣X-μ∣>3σ, where X is a data in the group, μ is the mean of the group, and σ is the standard deviation of the group. For non-normally distributed groups, the boxplot method is used to perform outlier screening and data cleaning. The outlier removal condition is X < Q1-1.5×IQR or X > Q3+1.5×IQR, where X is a single data point, Q1 is the first quartile, Q3 is the third quartile, and IQR is the interquartile range. The Shapiro-Wilk test method calculates the statistic based on the order statistics of the sample data and a specific weight formula. By quantitatively evaluating the deviation of the sample distribution from the theoretical value of the normal distribution, it accurately determines whether the data conforms to the normal distribution. The Z-score can be calculated through a simple formula, requiring only two statistical quantities: mean and standard deviation. It is suitable for quickly processing large-scale data. The boxplot has high efficient information compression ability and strong anomaly detection function, especially in scenarios that require quick grasp of data profile, comparison of multiple groups, or screening of outliers, it has an irreplaceable advantage.

[0023] Specifically, in step 4, k ∈ {-1.75, -1.25, -0.75, -0.5, -0.25, 0, +0.25, +0.5, +0.75, +1.25, +1.75}, and the percentile values include P5, P10, P20, P30, P40, P50, P60, P70, P80, P90, and P95, as shown in Tables 3 and 4: Table 3: Corresponding table of water permeability grouping and standard deviation deviation of grouting amount per meter

[0024] Note: The water permeability data is divided into n groups, and 1, 2, …, n are the group numbers. q1, q2, …, qn-1 are the water permeability values of the group nodes.

[0025] Table 4: Corresponding table of water permeability grouping classification and grouting amount per meter percentile

[0026] Note: The water permeability data is divided into n groups, 1, 2, … n is the group number, q1, q2, … qn-1 is the water permeability value of the group node.

[0027] Specifically, in step 5, when the certain coefficient of the average value of the grouting amount per meter is 0, the certain percentile value is P50, and the adjustment is performed as shown in Table 1, Table 1 Grouting slurry specific gravity adjustment table according to grouting amount per meter

[0028] The coefficient is 0, and the percentile value is P50, which represents an ideal value as a reference. When the deviation is large to the extent shown in the table, the classification is promoted.

[0029] Specifically, in step 6, the grouting effect evaluation evaluation standard is shown in Table 2, Table 2 Grouting effect evaluation table according to grouting amount per meter distribution characteristics

[0030] The grouting amount less area and the grouting amount more area should be the key areas for grouting effect test. For the grouting amount less area, it may be that the geological fracture is more developed than expected, and the target value is recalculated by switching to a higher level of water permeability group or using a backup grouting hole to form a surrounding circle. For the grouting amount more area, it may be that the grout is diluted / channeling by underground water, and the grouting is immediately stopped to check the pipeline sealing or replace the double liquid grout to accelerate the solidification or reduce the grouting pressure level.

[0031] Those skilled in the art of the present technology should recognize that the above embodiments are only used to illustrate the present application, and are not used as a limitation of the present application. As long as the changes and modifications of the above described embodiments are within the spirit and principles of the present application, they will fall within the scope of the claims of the present application.

Claims

1. A grouting process dynamic control method based on water permeability and statistical characteristics of grouting amount per meter, characterized in that It comprises the following steps: Step 1, data acquisition and pretreatment, the data acquisition includes pressure test data and grouting data, the pressure test data includes pressure, flow, segment length, the grouting data includes grouting dry weight, the data pretreatment includes permeability and per meter grouting amount calculation, the permeability calculation formula is: ； Wherein q is the permeability, Q is the flow, P is the pressure, L is the segment length; The per meter grouting amount calculation formula is: ； Wherein m is the per meter grouting amount, M is the grouting dry weight, L is the segment length; Step 2, data sorting and grouping, the calculated permeability data is sorted from small to large according to the numerical value, the corresponding per meter grouting amount data adjusts the position synchronously with the permeability sorting, when grouping, the number of groups is determined according to the total sample quantity, combined with the engineering practice, when the sample quantity is less, the number of groups is less, when the sample quantity is larger, the number of groups is increased accordingly: Step 3, data normality test and cleaning: Step 4, statistical model construction, for the normal distribution group: calculate several standard deviation deviation values of grouting amount per meter: V k = μ + kσ, wherein V k is the standard deviation deviation value, μ is the average grouting amount per meter of the group, σ is the standard deviation of the grouting amount per meter of the group, k is the deviation coefficient, and a permeability rate-grouting amount per meter standard deviation deviation value mapping table is established, and for the non-normal distribution group: calculate several percentile values of grouting amount per meter, and a permeability rate-grouting amount per meter percentile value mapping table is established, Step 5, grouting process dynamic control, (1) According to the data of water pressure test before grouting, the water permeability rate q of the current grouting section length is calculated current， (2) matching mapping table determines the belonging group and statistical characteristics, (3) Real-time calculation of cumulative grouting quantity per meter m current , , ΔL is the current cumulative length of grouting section, ∑M is the current cumulative dry material weight of grouting section, (4) Control decision, when m current <The average value of the coefficient of grouting amount per meter, or <The grouting pressure and grouting flow are executed according to the original engineering design, and the slurry specific gravity remains unchanged, when mcurrent≥The average value of the coefficient of grouting amount per meter, or ≥The corresponding percentile value is adjusted to execute, (5) grouting end determination condition: meet the grouting end standard in ground area treatment engineering design, Step 6, grouting effect evaluation.

2. The grouting process dynamic control method based on water permeability and per meter grouting amount statistical characteristics according to claim 1, characterized in that The data acquisition period in step 1 is in the stable pressure stage, the pressure value and flow value in the stable pressure stage are used as the calculation basis, the dry weight includes the total weight of cement, clay and fly ash.

3. The grouting process dynamic control method based on water permeability and per meter grouting amount statistical characteristics according to claim 1, characterized in that In step 2, the grouped permeability data is grouped by K-means clustering algorithm, and at least ensures that the sample quantity of each group is not less than 15.

4. The grouting process dynamic control method based on water permeability and per meter grouting amount statistical characteristics according to claim 1, characterized in that The normality test in step 3 uses Shapiro-Wilk test method to conduct normality test on the water permeability data and the grouting amount per meter data in each group respectively, the data cleaning uses Z-score method to carry out abnormal value screening and data cleaning on the normal distribution group, the abnormal value elimination condition is that ∣X-μ∣>3σ, wherein X is a data in the group, μ is the mean of the group, and σ is the standard deviation of the group, the abnormal value screening and data cleaning on the non-normal distribution group uses box plot method, the abnormal value elimination condition is that ∣X-μ∣>1.5×IQR, wherein X is a data in the group, μ is the mean of the group, σ is the standard deviation of the group, and IQR is the interquartile range. X Q 1- 1.5× IQR or X Q 3 +1.5× IQR, wherein X is a single data point, Q 1 is the first quartile Q 3 is the third quartile, IQR is the interquartile range.​​ 5. The grouting process dynamic control method based on water permeability and per meter grouting amount statistical characteristics according to claim 1, characterized in that As described in step 4 k ∈ {-1.75, -1.25, -0.75, -0.5, -0.25, 0, +0.25, +0.5, +0.75, +1.25, +1.75}, the percentile values include P5, P10, P20, P30, P40, P50, P60, P70, P80, P90, P95.

6. The grouting process dynamic control method based on water permeability and per meter grouting amount statistical characteristics according to claim 5, characterized in that As described in step 5 m current < A certain coefficient in the average of grouting quantity per meter is 0, a certain percentage value is P50, and the adjustment is performed as shown in Table 1, Table 1 Grouting slurry specific gravity adjustment table according to per meter grouting amount 。 7. The method according to any one of claims 1 to 6, wherein In step 6, the grouting effect evaluation evaluation standard is shown in table 2, Table 2 Grouting effect evaluation table according to per meter grouting amount distribution characteristics 。

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