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Classification of coal seam outburst hazards and evaluation of the importance of influencing factors

  • Xianzhi Shi EMAIL logo , Dazhao Song and Ziwei Qian
Published/Copyright: July 6, 2017
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Open Geosciences
From the journal Open Geosciences Volume 9 Issue 1

Abstract

Coal and gas outbursts are the result of several geological factors related to coal seam gas (coal seam gas pressure P, coal seam sturdiness coefficient f and coal seam gas content W), and these parameters can be used to classify the outburst hazard level of a coal seam.

To classify the outburst hazard level of a coal seam by means of statistical methods, this study considered the geological parameters of coal seam gas and statistical data on the amount of material involved in coal outbursts. Through multivariate regression analysis, a multivariate regression equation between the outburst coal quantity and P, f and W was established.

Using a significance evaluation of the aforementioned factors, the relative contributions of the gas-related geological parameters to the outburst hazard level of a coal seam were found to follow the order P>f>W.

This work provides a scientific basis for evaluating the outburst hazard level of a coal seam and adopting feasible and economical outburst-prevention measures.

Keywords: Outburst hazard classification; gas geological parameters; regression analysis; importance evaluation

1 Introduction

The study of coal seam outburst hazards has resulted in the classification of non-burst coal seams and outburst coal seams [1]. In the 1990s, given the demand for the classification of outburst coal seams, some scholars began to study the coal seam outburst hazard (hazard degree) [2-10] and to classify the coal seam outburst hazard degree (outburst degree) [5-9]. Yu adopted the Analytic Hierarchy Process (AHP) to divide the risky targets into 8 factors and used a matrix to define the importance of evaluation indices in coal seam outburst hazards [2]. Tang et al. proposed that the expansion energy of the initial releasing gas and the thickness of a soft coal seam played a role in the loss of stability of a spherical shell losing during the outburst process. Using an outburst simulation experiment, a linear function was established to predict the outburst intensity [6]. Ma et al. proposed the Attribute Synthetic Evaluation Model to predict the risk of a coal and gas outburst with six indices based on attribute mathematics theory [7]. You et al. established a fuzzy evaluation model to evaluate the coal and gas outburst hazard by means of fuzzy mathematics theory [11]. Gui and Yu established an outburst prediction model by employing the grey system theory and calculating the grey relation for each of the studied factors [12]. In another study, eight factors, including active tectonics, maximum principal stress, gas pressure, gas content and so on, were the main criteria used to predict coal and gas outbursts, coal seam outburst danger points, coal and gas outburst danger areas, and the boundary between dangerous and safe areas and were implemented via the pattern recognition method [13]. Using the RFPA2D-Flow software and a similar simulation test method, Yuan studied groups of coal bodies under real stress and gas pressure conditions [14]. Li established a model based on backpropagation neural network theory and grey relational theory to analyze the risk of coal and gas outbursts [15]. Kong et al. used the total efficacy coefficient value as the evaluation standard and determined the coal seam protruding threat level [16]. Sun et al. established a model for the prediction of working face outbursts based on distance discriminant analysis through use of the electromagnetic radiation prediction index (intensity and pulse number of electromagnetic radiation), the conventional R index, borehole gas emission velocity and quantity of drill cuttings [17]. Wu et al. used the grey correlation analysis of a grey theory mathematical model to quantitatively analyze the six types of indicators of coal and gas outbursts and determined the best predictor [18]. Liu et al. analyzed the sensitivity of forecasting indices of coal and gas outbursts and used grey relational theory to optimize the sensitivity of the Jiulishan coalmine gas outburst prediction index [19]. Some mines in China involve low permeability coal seams, according to practical production experience. As a result, the sturdiness coefficient of a coal seam is indirectly used in the development of coal and gas outburst technology, which has achieved rather good economic benefits and technological advancement [20].

Although some achievements have been obtained, previous studies suffer from difficulties associated with evaluation index collection (furthermore, the evaluation indices themselves vary substantially) or complex calculation methods [2, 7, 8], which makes it difficult for on-site engineers and technicians to evaluate the outburst hazard. In addition, most of the above-mentioned studies used a single index for the calculation, which always results in an imprecise evaluation of the hazard level [9]. Therefore, for coalmines with outburst hazards, and especially for engineers and technicians in such coalmines, it is imperative to classify the outburst hazard level (hazard intensity) of a coal seam to better select scientific and economically practical outburst-prevention countermeasures.

A coal seam outburst is a comprehensive result of many factors [2, 8? , 9] including the coal seam gas pressure (geological stress), coal strength (sturdiness coefficient of the coal seam), coal seam gas content, fracture type of the coal seam, initial velocity of the diffusing gas, tectonic structure, coal seam thickness, and depth of the coal seam. Ma et al. adopted a comprehensive index of D and K to evaluate coal and gas outbursts [7]. In adopting the coal seam thickness index, Zhang used gas-expansion energy to categorize the outburst hazard of a coal seam [8]. For the practice of mining coal seams with outburst hazards, statistical data and experiments show [5-9, 21-27] that coal and gas outbursts are the combined result of geological stress (gas pressure), coal seam gas content, and coal body strength and that other factors are all related to or derived from these three parameters. Among them, the failure type of the coal seam, which results from tectonic movement, constitutes the outward sign of a decrease in the coal body strength [5-9, 21-25]. The failure degree reflects the coal body strength. Therefore, the strength value of the coal body reflects its failure type. The tectonic structure of an area controls where geological stress converges [22, 23], and the tectonic stress in a coal seam is reflected in the gas pressure and coal strength indices. The initial velocity of the diffusing gas is a factor of the coal seam sturdiness coefficient.

To provide a simple and convenient method for the evaluation of the outburst danger of a coal seam, this study investigated the relationship between a coal and gas outburst and the primary gas-related geological parameters of the coal seam, including the coal seam gas pressure P, sturdiness coefficient f and gas content W. To achieve this goal, statistical theories and methods were employed. The relationship between these indices and the amount of material involved in outbursts was found by using univariate regression analysis and multivariate regression analysis to determine the hazard degree (outburst hazard level) of coal seams. Then, the importance of gas-related geological parameters contributing to the outburst hazard was analyzed, and a multivariate regression equation between the outburst coal quantity and P, f and W was established. The results of this study may provide a theoretical basis for the prediction of coal and gas outburst danger and may help prevent outbursts through the future development of technology that controls and prevents coal and gas outbursts.

2 Methods

2.1 Theoretical background

Univariateregression. Univariate regression analysis can reflect the linear correlation between a dependent variable and an independent variable [28]. Therefore, it can be used to confirm the correlation relationships between the outburst intensity (outburst coal quantity) and other factors. Univariate regression equations are generally established as follows: A scatter diagram of n pairs of data in rectangular coordinates is drawn to observe whether they show a linear distribution trend. If two variables show such a trend, a one-dimensional linear equation can be established, and the dependent variable can then be predicted based on the independent variable.

Multivariate regression. The principle of the multivariate regression is that it supposes a linear relation exists. In this study, the relationship exists between the outburst coal quantity y and the m independent variables x1, x2, . . . , xm:

y=b0+b1x1+b2x2+...+bmxm+ξ(1)

where b0, b1, b2, . . . , bm are regression coefficients.

Suppose n groups of sample observation data:

{X11,X12,X13...,X1m,Y1X21,X22,X23...,X2m,Y2X31,X32,X33...,X3m,Y3........................................Xn1,Xn2,Xn3...,Xnm,Yn(2)

where xij indicates the ith observation value of xj; therefore:

{y1=b0+b1x11+b2x12+b3x13+...+bmx1m+ξ1y2=b0+b1x21+b2x22+b3x23+...+bmx2m+ξ2y3=b0+b1x31+b2x32+b3x33+...+bmx3m+ξ3........................................................................yn=b0+b1xn1+b2xn2+b3xn3+...+bmxnm+ξn(3)

where b0, b1, b2, . . . , bm are m+1 undetermined parameters and ξ1, ξ2 , . . . , ξn are n independent random variables that obey the same normal distribution N (0, σ2). Thus, Equation 3 is a mathematical model of a multivariate linear regression.

The linear regression model between P, f and W and outburst coal quantity is as follows:

{y1=b0+b1P1+b2W1+b3f1+ξ1y2=b0+b1P2+b2W2+b3f2+ξ2y3=b0+b1P3+b2W3+b3f3+ξ3..................................................yn=b0+b1Pn+b2Wn+b3fn+ξn(4)

2.2 Data collection and sorting

To study the impact of the P, W and f on the outburst hazard degree of coal seams, coal and gas outburst cases in the main mining area affected by outbursts in China, including Sichuan [2, 5], Henan [4], Anhui [5], Guizhou [7], Hunan, Jiangxi and Shanxi [9], were investigated. These provinces are the mainly coal and gas outburst-existing regions in China, and more than 90% of the coal and gas outburst accidents in China have occurred in these provinces. The outburst data from the Gagarin mining area in Donbas in the former Soviet Union were also used [1], as these outburst cases and the experience in preventing outburst accidents in this region are commonly cited in China. The statistical data are shown in Table 1.

Table 1

Outburst coal quantity and gas-related coal seam geological parameters

Regiony(t)P

(MPa)		W(m3/t)fRegiony (t)P

(MPa)		W (m3/t)f
Guizhou4901.6513.90.331894211.700.12
6552.0816.70.479701.611.700.19
8892.2217.40.3723971.7433.200.16
6791.1515.40.2515002.4420.000.43
25992.5250.20Henan3151.1916.600.98
10101.6518.40.2510701.8116.600.82
15711.812.10.22360.6212.10.484
25001.23120.311300.769.500.33
3272.0619.10.122351.1617.700.21
15711.812.070.2210211.72130.2
3272.0619.10.12Hunan12672.07150.24
850.929.20.9114201.63120.26
2001.02100.35Liaoning40827.422.500.26
4081.111.450.4353906.222.500.26
300.7813.50.47Jiangxi3004.621.580.61
Anhui105004.121.750.173200335.50.18
4362.3150.293181.279.110.30
7832.616.10.21Shanxi2600.411.220.21
3742.716.10.2930002.423.320.32
1700.665.20.73Sichuan52704.411.290.17
1100.74.60.7387655.01250.18
2502.413.10.8631094.95240.18
10143.8714.390.21Donbas in145005.0200.14
6002.413.10.64the former
5071.5315.20.61Soviet Union

y, outburst coal quantity; P, gas pressure; W, gas content; f, coal seam sturdiness coefficient.

2.3 Data analysis

The Statistical Package for the Social Sciences (SPSS) software displays management and analytical data through windows on the computer screen. It has been extensively applied for scientific research statistics and is easy to manipulate. In this study, SPSS was used to solve the multivariate regression equations, according to the method in the literature [29].

3 Results and discussion

3.1 Univariate regression analysis

An analysis of the statistical data revealed that the quantity of outburst coal (rock) increases with the P and W at the time the coal and gas outburst occurs (Figures 1 and 2). The outburst quantity tends to increase as f decreases (Figure 3). A univariate regression analysis was thus adopted, and Microsoft Excel was used for the regression analysis [30]. The regression equation for the outburst coal quantity and P is as follows:

Figure 1 Correlation curve of the outburst coal quantity and coal seam gas pressure
Figure 1

Correlation curve of the outburst coal quantity and coal seam gas pressure

Figure 2 Correlation curve of the outburst coal quantity and coal seam sturdiness coefficient
Figure 2

Correlation curve of the outburst coal quantity and coal seam sturdiness coefficient

Figure 3 Correlation curve of the outburst coal quantity and coal seam gas content
Figure 3

Correlation curve of the outburst coal quantity and coal seam gas content

y¯=275.99x1.6568(R=0.76)(5)

The regression equation of f is as follows:

y¯=137.8lx−1.4705(R=0.59)(6)

The regression equation of W is as follows:

y¯=2.9836x2.0669(R=0.59)(7)

The correlation coefficient R=0.76 indicates that the outburst coal (rock) quantity is very significantly correlated with the P, and R=0.59 indicates that it is also remarkably correlated with f and W of the coal seam. These results are consistent with those reported in literature [5-9]. Therefore, these variables can be used to classify the outburst hazard level (outburst intensity) of a coal seam. Based on these results in combination with the coal seam outburst intensity and statistical data, the outburst hazard level of a coal seam can be classified into four categories: a low level of outburst risk, a medium level of outburst risk, a high level of outburst risk and an extremely high level of outburst risk (Table 2).

Table 2

Classification of the outburst hazard level of a coal seam based on the coal seam gas pressure, sturdiness coefficient and gas content

Outburst hazardLowMediumHighExtremely

high
Outburst coal (rock) quantity (t)<100100-500500-1000≥1000
Gas pressure P (MPa)0.4-0.540.54-1.431.43-2.18≥2.18
Coal seam sturdiness coefficient f>1.241.24-0.420.42-0.26≤0.26
Gas content W(m3/t)<5.525.52-12.0712.07-16.92≥16.92

A coal and gas outburst in a coal seam is the comprehensive result of P, f and W. A single factor can reflect the hazard level of a coal seam; however, a single factor cannot objectively reflect its own importance in the coal and gas outburst or indicate the contributions of other factors to the coal and gas outburst. Therefore, multivariate regression analysis [28] was used to evaluate the contributions of the aforementioned gas geological parameters to the outburst hazard level.

3.2 Multivariate regression analysis

The regression relationships between the outburst coal quantity and P, f and W were used to develop an expression relating the outburst coal quantity and the aforementioned factors to demonstrate the importance of each factor in the classification of the coal seam outburst hazard.

The data in Table 2, multivariate regression analysis and SPSS software were used to solve the multivariate regression equation [29]. After the data were input into the SPSS software, the parameters in Tables 3-6 were derived using the multivariate linear regression analysis.

Table 3

Variable screening strategy

ModeVariablesVariables RemovedMethod
1f, P, WEnter

a. All requested variables entered. b. Dependent variable: y.

Table 4

Model summary

ModeRR SquaredAdjusted R SquaredStd. Error of the Estimate
10.7080.5010.4652125.76604

a. Predictors: (Constant), f, P, W

Table 5

Variance analysis

ModeSum of squaresdfMean squareFSig.
1 Regression1.864E836.212E7 13.7460.000
Residual1.853E8414518881.244
Total3.716E844

a. Predictors: (Constant), f, P, W; b. Dependent variable: y.

Table 6

Coefficients

ModeUnstandardized coefficientsStandardized coefficientstSig.
BStd. ErrorBeta
1 (Constant)-372.5951214.8020.605-0.3070.761
P1247.000267.1990.0444.6670.000
W20.75561.845-0.1860.3360.739
f-2371.3351503.553-1.5770.122

a. Dependent variable: y

According to the computational data analysis implemented in SPSS software, the result of regression equation F indicates that the regression equation is significantly correlated at a significance level of α=0.01. Therefore, the regression equation between the outburst coal quantity and P, f and W is

y¯=1247P+20.755W−2371.335f−372.595(8)

To validate the efficiency of the multivariate regression equation, the outburst coal quantity at the Tonghua Coal Mine, Chongqing, China, was calculated (P=2.4 MPa, f=0.15, and W=23.23 m3/t), and the predicted result was 2747 t. This result was basically consistent with the real coal quantity at the time of the outburst accident in this area on May 30, 2009 (3000 t) [9].

3.3 Importance evaluation of the factors

Although the outburst coal (rock) quantity (y) is significantly correlated to the P, f and W at a significance level of α=0.01 in the regression equation, the levels of importance of the three factors in an outburst are not identical. According to the size of the absolute values of P, f and W in the t-test, the contributions of the three factors to the coal and gas outburst follow the order P>f>W in terms of size.

The key aspect of outburst hazard evaluation is the identification of sensitive indices in the evaluation region from among the various evaluation indices. Based on the sensitivity analysis of the outburst hazard evaluation index via principal component analysis, the sensitivity of P was stronger than that of f [31]. A gray correlation analysis of the importance of the factors involved in coal and gas outbursts shows that the effect of P is greater than that of f [32]. However, to the best of our knowledge, no studies have taken the sensitivity of W into account.

The contributions of the three factors driving coal and gas outbursts in coal seams support previous research results [2, 22, 23, 33, 34]. P, which is the main factor controlling coal and gas outbursts, plays a dominant role. Variation in f often depends on the geological structure. This term noticeably decreases at the structure ruptures where the coal seam breaks. Therefore, the importance of f can indicate that an outburst is likely to occur in a coal seam [20, 22, 23, 33, 34]. In a closed space, a high W in a coal seam can lead to an increase in P, and the fluidity of the gas, which lubricates cracks and fissures in the coal, decreases the strength of the coal body. The arrangement of the three factors in terms of their size indicates the different roles played by these three factors in coal and gas outbursts. P plays a major role, f plays a moderate role, and W plays a minor role in outbursts.

4 Conclusions

This study drew the following conclusions.

  1. Through statistical analysis of the relationships among the gas-related geological parameters of coal and gas outbursts, in combination with the results of previous research, the primary gas-related geological parameters influencing gas outbursts are coal seam gas pressure P, sturdiness coefficient f and coal seam gas content W.

  2. A univariate regression analysis is employed to classify the hazard level of a coal seam outburst and its corresponding gas geological index value. The indices used to evaluate the risk of a coal seam outburst and their complicated calculations are simplified, improving the feasibility of practically applying this classification. Additionally, the gas geological parameters are employed to conveniently calculate the coal (rock) quantity (outburst intensity) when an outburst occurs, thereby providing a scientific approach for evaluating the magnitude of outburst disasters.

  3. Statistical data related to coal and gas outbursts are utilized to establish a multivariate regression model of coal and gas outbursts. With SPSS software, P (crustal stress) was shown to be the primary factor in coal and gas outbursts, followed by f. W plays a lesser role in coal and gas outbursts.

These results provide a theoretical foundation for the main technological parameters used to predict coal and gas outburst hazards and for testing outburst-prevention measures. Additionally, they indicate a clear direction for the future development of prevention and control technologies for coal and gas outbursts. This study provides scientific methods for evaluating the outburst hazards of outburst coal seams in coalmines and outlines reasonable regional outburst control and prevention measures for coalmines according to the outburst hazard of a coal seam and the technological support needed to reduce outburst-prevention costs.

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Received: 2016-1-4
Accepted: 2017-5-16
Published Online: 2017-7-6

© 2017 Xianzhi Shi et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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https://doi.org/10.1515/geo-2017-0024
Keywords for this article
Outburst hazard classification; gas geological parameters; regression analysis; importance evaluation
Creative Commons
BY-NC-ND 3.0

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