DEVELOPMENT OF MULTIPLE REGRESSION FUNCTIONS 

FOR PERFORMANCE PREDICTION OF GOB GAS VENTHOLES FOR 

SEALED AND ACTIVE LONGWALL MINES 

Heather N. Dougherty, NIOSH, OMSHR 

C. Özgen Karacan, NIOSH, OMSHR 

Gerrit V.R. Goodman, NIOSH, OMSHR 

National Institute for Occupational Safety and Health, Office of Mine Safety and Health 

Research, Pittsburgh, PA 15236, USA 

Abstract 

In the absence of values for each influencing parameter and a known relationship of input-output space, novel 

approaches should be implemented as proxy solutions to mathematically complicated problems. Proxy solutions can 

be in the form of linear or non-linear multiple regression equations. Predicting the performance of gob gas ventholes 

is one of these mathematically complicated problems as it is difficult to know the exact relationships between 

various parameters influencing gob gas venthole (GGV) performance from active and sealed (completed) longwall 

panels. In this paper, a discussion of the NIOSH-developed software suite for methane control and prediction 

(MCP) based on an artificial neural network (ANN) is presented. The application of the ANN-based software suite 

to develop algebraic relationships for gob gas venthole performance prediction in sealed and active longwall panels 

is sought. 

Output information obtained from the NIOSH-MCP software as a response to various input parameters were used to 

create an input-output database for total flow and methane percentage from GGVs operating in active mines during 

completed and sealed panel situations. Due to the multi-variable nature of the software, outputs generated using the 

software for various inputs were used to develop analytical equations first using linear and then second using nonlinear multivariable regression techniques. The advantage of this method is its ability to better understand the 

significant parameters of the relationships between the inputs and outputs for gob gas venthole production in active 

and sealed panels and to be able to predict their performances using relatively simple analytical functions. 

1. Introduction 

Longwall mining, which became increasingly 

popular in the late 1970’s has become the most 

widely used method in coal and accounts for more 

than 50% of the United States underground coal 

production (EIA, 1995). Longwall mining has 

progressed since then to become increasingly 

mechanized, productive and safe. With these 

advancements there have been hurdles to overcome 

due to the rapid creation of gob and gases released 

from the broken strata. 

GGVs have been a very important part of longwall 

mining in numerous coal seams due to their 

effectiveness of eliminating gas within the fractured 

overburden or gob, reducing the effect of methane in 

the bleeder system. Other than increasing ventilation 

flow into the bleeder system, there are few ways to 

ventilate the gob so methane from the overburden 

does not flow into the active mine workings. Other 

than ventilation, GGVs are currently the best defense 

against methane escaping into the active mine. GGVs 

were initially just holes drilled into the gob prior to 

longwall mining to let gob gases escape, but they 

have evolved by adding a negative pressure pump to 

pull gases out of the gob. 

During the initial project planning phase of a mine, 

creating a degasification plan, determining the need 

for GGVs and anticipated concentration and flow of 

methane can be difficult. Degasification plans 

1
require a good knowledge of the coal and mining 

conditions to determine what types of degasification 

techniques, if any, are needed to safely mine the coal 

and ventilate the mine. GGV’s have become an 

important part of degasification plans, along with 

inseam boreholes, surface inseam boreholes, and the 

main mine ventilation system. Gob gas ventholes’ 

methane production potentials may be determined by 

looking at mining parameters such as percentage of 

panel completed, linear advance rate and 

characteristics of the GGV such as surface elevation , 

average overburden, slotted casing diameter, slotted 

casing distance to the seam, and GGV distance to 

tailgate, to name a few. 

Mine operators primarily use past practices and 

experiences to develop a degasification plan for their 

operations. Recently, there have been more 

complicated methods used to predict production 

performance of GGVs. Ren and Edwards (2002) 

suggest the use of computational fluid dynamics 

(CFD) modeling and geotechnical modeling to help 

understand and improve gas capture effectiveness. 

This paper centers on maximizing the effectiveness 

of production of GGVs to maximize gas extraction. 

Tomita etal (2003) describes a finite element model 

program created to predict gas emission based on 3D 

stress called Mine Gas Flow 3D (MGF-3D). Karacan 

et al. (2005) has developed 3D models to predict 

incremental changes in methane emissions due to the 

increasing panel width to optimize GGV 

performance. Investigating the effects of various 

GGV completion parameters, Karacan et al. (2007) 

used a reservoir simulation to estimate performance. 

These models (Karacan etal, 2005 and 2007) are 

realistic but difficult to build and not practical for 

modelers due to the moving inner-boundary 

conditions, for instance the existence of a progressing 

longwall face, used in their formulation. 

The objective of this work is to find simple-to-use 

analytical models to predict methane concentration 

and production from longwall GGVs for better mine 

safety and planning. NIOSH-MCP and both linear 

and nonlinear multiple regression techniques can 

create these models. Although MCP can determine 

the output values used in this study, the ANN 

systems it was built upon takes the information and 

calculates an output without the user knowing the 

relationship between variables. Determining these 

relationships is important in understanding GGV 

production; as it quantifies the sensitivity of methane 

production and methane quality for different mining 

and GGV parameters. 

Using the data from MCP, linear and non linear 

modeling will be used to establish model equations 

determining the relationship between the variables 

and the outcome of the different inputs. Linear 

modeling will initially be used because it is more 

straight forward and easier to do. The data will be 

looked at in all aspects, but mostly the residuals, to 

see if the calculated data fits to the actual data. If this 

fitting is not sufficient non-linear modeling will be 

used to find a model that fits the real data to a degree 

making the residuals lower and make the calculated 

data a better fit to the real data. 

2. MCP modeling 

The data for this paper was obtained using NIOSH 

Methane Control and Prediction (MCP) software to 

simulate different longwall development and GGV 

parameters and collect output values. MCP was built 

using a multilayer perceptron (MLP) type artificial 

neural network (ANN) structure and actual data was 

used to train the ANN. The ANN was trained using 

different geographical locations, longwall operation 

parameters, and coalbed characteristics as input and 

was tested to classify the outputs (Karacan 2008a; 

2009a; 2009b; 2009c). 

MCP was developed with four modules: 

1. Coal measure rock mechanical properties 

prediction 

2. Mine ventilation emission prediction 

3. Degasification system selection 

4. Gob gas venthole production performance 

prediction 

These four modules help mine operators define future 

degasification for a safer and less gassy mine. In this 

study only the gob gas venthole production 

performance prediction module was used to produce 

values that were used during the regression analysis. 

Mining parameters or input values required for the 

MCP software were created by random number 

generation in MS Excel for each variable using its 

mean values and standard deviations. A correlation 

analysis was also performed on the randomly created 

input data set to check correlations between different 

variables. No correlations were found using the MS 

Excel correlation function. Input values were then 

used in the MCP program to obtain the corresponding 

outputs of GGV production rate and GGV percent 

methane. 

Table 1 shows the input parameters for MCP used to 

obtain flow rate and methane concentration data 

output. The input-output data produced were 

2
analyzed using both multiple linear and non-linear 

regression methods to determine their relationships. 

These analyses and the results are described in the 

following sections. 

Table 1. MCP input parameters, minimum, 

maximum’s and mean used within the MCP program. 

Panel Status 

Active Completed 

Face Status 

Idle Advancing 

Parameter Min Mean Max 

% of Panel Completed 

PC 1 73.2 100 

Linear Advance Rate 

(ft/day) LA 0 25.1 96 

Surface Elevation (ft) 

SE 1000 1280 1460 

Average Overburden 

(ft) AO 600 743 1000 

Casing Diameter 

(in)CD 6 7.5 9 

Slotted Casing Distance 

(ft) SC 30 45 60 

GGV Distance to 

Tailgate (ft) DTG 220 280 340 

GGV Distance from 

Start (ft) DF 350 2160 8000 

LW Panel Length (ft) 

PL 5000 10900 11500 

LW Panel Width (ft) 

PW 1200 1250 1450 

Barometric Pressure (in. 

Hg) BP 15 28.6 30 

Exhauster Vacuum (in. 

H2O) EV 0.5 -43 -90 

3. Multiple variable models for prediction of GGV 

production and methane concentration 

In this paper, the longwall panel and GGV placement 

parameters shown in Table 1 that control GGV 

production and methane concentration were 

considered as independent parameters. All of these 

parameters were randomly varied and entered in the 

MCP program to generate an output that was broken 

down into two categories for the longwall panel 

status: active and completed panel. Only active – 

advancing and complete – idle were run for this test, 

since active – idle occurs on a rare occasion, and 

complete – advancing is not a possibility due to the 

fact that a complete panel cannot advance. For each 

panel status, both methane concentration and gas 

production were studied for their relationships with 

input parameters. 

3.1 Multiple linear modeling 

Dependent variables used for linear regressions were 

simulated GGV production rate and methane 

concentration given a panel status of active or 

complete. Using the 12 independent parameters 

listed in Table 1, 118 and 131 observations 

(simulations) were created for active and completedpanel cases, respectively. A separate regression 

analysis was performed for each of the longwall 

statuses presented. Table 1 shows the symbolic 

names of all the variables used in the multiple linear 

regression analysis. 

Backward elimination was used as the regression 

variable selection technique in this study. Backward 

elimination has advantages over forward selection 

and stepwise regression by evaluating the joint 

predictive capability of the inputs (Motulsky and 

Christopoulos, 2004). Analysis of variance or 

ANOVA, as the associated analysis used in backward 

elimination, evaluated the importance of each 

independent variable based on the contribution it 

makes to the prediction of the dependent variable. 

This statistical technique was utilized to develop a 

linear model to predict GGV production and methane 

concentration using a minimum of 118 observations 

for the two different longwall statuses. While 

selecting the longwall and GGV parameters for their 

importance to GGV production and concentration, it 

was important to be sure there would not be a high 

degree of correlation among the variables. 

Throughout the analyses the confidence interval of 

regression was set as 95%. 

The GGV production and methane concentration 

modeling was started by including all of the input 

parameters. However, not all of them survived the 

backward elimination process under the given 

constraints of confidence interval and significance 

values. Variables whose statistics proved them to be 

insignificant in the presence of the other significant 

ones were excluded during backward elimination so 

final models were composed only of significant 

variables. Beyond the statistical importance of the 

data, it is important from a practical point of view 

because fewer significant variables make data 

collection easier, economical, and understandable. 

Description of parameters in Table 1, the panel status 

and their possible effects are given in Appendix A. 

For more detailed information, please see Karacan 

(2009c; 2009d). 

3
3.1.1. Model equations, analysis of multiple

determination coefficients, and ANOVA 

Linear models were run for GGV production and 

methane concentration for the two longwall statuses 

derived using backward elimination. Variables were 

checked and the multiple determination coefficient R 

and the square of multiple determination coefficient 

R2

 were calculated (Karacan, 2008b). 

The outputs of ANOVA (analysis of variance) tests 

are also analyzed. ANOVA gives general

significance statistics data and possible

autocorrelation problems within the data set, which 

do not disclose the regression accuracy of the models. 

Accuracy of the models can be revealed by

regression coefficients such as determination

coefficient R and the square of multiple

determination coefficients R2

 (Karacan, 2008b). The 

values of R2

 or the coefficient of determination is 

used to shows how the variability of the data set is 

accounted for by the model. The R2

 value for 

methane concentration ranges from 0.7464 to 0.5531, 

and the value for GGV production rates ranges from 

0.6343 to 0.5531 based on panel status (active to idle, 

respectively). However, the model fit to GGV 

production rate and methane concentration in the 

active panel case is higher compared to the model for 

production rate from a completed panel. This

indicates that the GGV production rate and methane 

concentration from active panels are more linearly 

related to various borehole completion and face

advance parameters. These values show that methane 

concentration can be modeled more successfully with 

a multi-linear model. However, to improve the

predictive capability, non-linear models were

investigated for active and complete statuses. 

Figure 1A and 1B show the comparison of MCP 

results (actual data) to outputs for the linear models 

for methane concentration in GGV gas production 

and production rate from a completed panel,

respectively. An accurate linear model would show 

all the points very close to the fit line shown, which 

has a 45o

- slope or perfect prediction line, and would 

indicate an accurate fitting of the calculated and 

actual data. Active panel plots show similar

distribution. 

Final linear models do not contain all of the

parameters listed in Table 1 and discussed in

Appendix A. Final models contain 4 to 7 of the 

initial 12 parameters. The three parameters that were 

excluded from all four equations were linear advance, 

surface elevation and average overburden, due to 

their low significance determined during the 

backward elimination process. 

1-A

1-B

Figure 1. Comparison of actual methane 

concentration versus calculated methane 

concentration (1-A) and a comparison of actual GGV 

production versus calculated GGV production (1-B) 

for a completed panel. 

Surface elevation and average overburden can be 

used similarly when referring to a horizontal deposit 

of coal; these two parameters would show numbers 

similar to the depth of the GGV. Similarly, linear 

advance rate was determined to be insignificant 

compared to the other variables whose statistics 

proved them to be more significant. Linear advance 

may be related to progression of the gob and to 

whether it is created more slowly or quickly. 

Therefore, face advance rate may not have a 

significant impact on GGV production rate and 

4
methane concentration once the GGV has been 

actively undermined. 

The parameter “percent completed” impacted two of 

the four equations – which points to general progress 

of the panel and expansion of the gob as being 

important. The most significant variables used in 

three out of the four models were casing diameter, 

longwall panel width, and outside atmospheric 

pressure. Each of these variables has a different 

effect on GGV performance. Casing diameter seems 

to have the most effect on production – the easier the 

gas can flow from the gob to the surface, the greater 

the production. However, everything else being the 

same, increasing casing diameter results in a 

decreased methane concentration due to creating a 

larger sink and drawing additional mine air from the 

gob into the borehole, as shown by numerical 

reservoir simulations (Karacan et al., 2007). 

Likewise, the longwall panel width had a larger effect 

on gas production rate and methane concentration 

since this parameter is related to the gob size. Outside 

barometric pressure has a large effect on the 

ventilation of the whole mine, thus making it easier 

or harder for the exhauster to work to liberate the 

methane gas from the gob environment. 

3.1.2. Analysis of the multiple linear regression 

coefficients 

Although multiple linear methods can model 

production rates and methane concentrations from 

GGVs, comparison of MCP data with the multiplelinear model shows that predictability could be 

improved (Figure 1-A and B). Figure 2 shows 

standardized residuals versus predicted values for 

production rates calculated for an active panel. In a 

case where the multiple linear model is adequate and 

can represent the data accurately, the standard 

residuals should be distributed randomly along the 

predicted value axis between +2 and -2. Figure 2 

shows that there is evidence of increasing error 

variance with increasing predicted values. However, 

as seen from this figure, the standard residuals are 

distributed as a well defined arc shape, which is an 

indication of nonlinearity: the production rate and 

methane concentrations are dependent on the 

variables given in Table 1 in a non-linear way. 

Therefore, application of non-linear regression 

techniques is a more promising way of modeling the 

rate and concentration data. 

Figure 2. Residual scatter plot for GGV production 

in an active longwall panel for linear modeling 

3.2. Non-linear regression modeling and analysis 

of GGV production rate and methane 

concentration 

As shown in Figure 1A and 1B, the predicted rates 

and concentrations do not exactly follow the 45o

 line 

when compared with the MCP data, showing the 

degree of accuracy with linear models can be 

improved by other techniques. Therefore, non-linear 

regression was performed as the next step to model 

the data. As in the multiple linear study the same 

input-output data were used for both GGV flow rates 

and methane concentrations for each longwall panel 

status. 

Non-linear regression was also performed using the 

principles of least squares. During the regression 

process, the goal is to minimize the sum of squared 

error (SSE) and mean squared error (MSE) of the 

non-linear equation to find the equation closest to the 

actual data. However, in order to find the distances to 

actual data, derivatives of the proposed equations are 

taken for each point numerically using iterative 

techniques. This makes modeling and analysis more 

complicated and difficult. However, unlike multiple 

linear regression, if one knows the general functional 

relationships between dependent and independent 

variables, a successful nonlinear regression or model 

can actually provide insight to the physics of the 

process. 

In this study polynomial-type non-linear equations 

were used as models. Special attention was paid to 

select the type and degree of the equations so that the 

model would provide a satisfactory regression, but 

not too complicated for practical use. As a result of 

various trials with different model equations, fourth 

5
 

6

order polynomials were selected as the best equations 

to meet these two criteria. After the regression study, 

coefficients were examined and ones with either zero 

or less than 10-4 were eliminated from the final 

proposed models (Table 2) as they indicated 

insignificant contributions. 

Table 2 represents the model equations that were 

obtained from the nonlinear regressions. Along with 

the model equations for both production rate and 

methane concentration for active and completed 

panels, Table 2 shows R2

, sum of squared error 

(SSE), mean square error (MSE), and root mean 

square error (RMSE). SSE, MSE and RMSE are 

presented to show that, although a higher value of R2

indicates a better fit, it does not always mean that the 

residuals are low. SSE, MSE, and RMSE are all 

ways to determine the error within model equation. 

SSE, or sum of squared error, is the sum of the 

squared errors from each set of data. MSE or mean 

square error is the square of the residuals from the 

actual answer to the calculated answer. Both SSE and 

MSE are in units of the square of the units of the data 

for the model which in this case is MMscf/d for 

methane production and % CH4 for methane percent. 

RMSE or root mean square error is the square root of 

the MSE which determines the width of the 

confidence intervals for the models and its units are 

the same as the units of the data for the model. For 

residual analyses and for deciding whether the 

predictions of an equation are close to the actual data 

points, analyses of squared errors and mean squared 

errors are more appropriate as they rely on the values 

of residuals which are also expected to be low for a 

good fit. 

Table 2 shows that R2

 values are higher than the ones 

developed in multiple linear regression, especially for 

the completed panel case that had been emphasized 

in the previous section for more prominent non-linear 

characteristics of the input-output data. Also, Table 2 

shows that various error measurements based on 

residuals are low for the simple non-linear models 

proposed for these complicated problems. 

Figure 3A and 3B illustrate comparisons between 

calculated and actual data plotted for (A) active 

longwall methane concentration and (B) active 

longwall methane production. These figures show a 

better fit when compared with MCP data than linear 

modeling allowed. Completed panel data show 

similar results. 

3-A

3-B

Figure 3. Non-linear plot of a comparison of actual 

vs. calculated data for methane concentration (3A) 

and GGV production (3B) for an active panel. 

4. Production characteristics of a GGV drilled 

over a completed panel 

Completed panels exhibit different reservoir 

characteristics compared to active panels in terms of 

gas availability and boundary conditions. For 

instance, an active panel may behave as an infinite 

reservoir since additional gob is created as long as the 

face advances, which makes additional gas available 

to be produced continuously. Existing boreholes may 

not experience boundary effects present in completed 

and sealed panels (Karacan, 2009d). Since active 

panels are ventilated continuously, the proximity of 

the bottom of the boreholes to the caved zone and 

suction vacuum may make a difference in terms of 

capturing mine air, which is not an issue in 

completed and sealed panels. These and other critical 

 

 
differences in reservoir and production potential from 

active versus completed panels may make a 

difference how GGVs should be drilled and operated. 

The equations given in Table 2 may help define the 

production characteristics of GGVs from completed 

panels in comparison to active panels and how 

various parameters may affect the production rate and 

methane concentration from completed panels. 

Table 2. Non-linear models for predicting GGV production and methane concentration for active and complete 

longwall status using longwall and GGV parameters as listed in Table 1. 

Longwall 

Status Model Equation R2 SSE MSE RMSE 

Active 

GGV 

PROD 

208.477 - (2.593 * PC) - (7.751 * CD) - (1.654 × 10-2 * PL) - 

(0.318 * PW) + (6.102 × 10-3 * EV) + (3.938 * PC2

) + (1.088 * 

CD2

) - (1.943 * PC3) - (4.895 × 10-02 * CD3) 

0.786 1.537 0.014 0.117 

Active 

METHANE 

CONC 

71.602 - (4.364 × 10-3 * PC) - (0.388 * SC) + (0.265 * DTG) - 

(1.542 × 10-2 * PL) - (8.707 × 10-2 * PW) - (1.062 × 10-2 * EV) + 

(0.455 * PC2

) + (9.023 × 10-3 * SC2) - (9.369 × 10-4 * DTG2) - 

(0.401 * PC3

) 

0.756 0.813 0.007 0.086 

Complete 

GGV 

PROD 

285.110 - (8.946 × 10-2 * PL) - (8.978 × 10-2 * AO) + (14.264 * 

CD) + (0.728 * SC) - (8.868 × 10-2 * PW) - (12.662 * BP) + 

(2.034 × 10-2 * EV) - (3.074 * CD2

) - (2.924 × 10 -2 * SC2) + 

(0.843 * BP2

) + (9.872 × 10-4 * EV2) + (0.292 * CD3) + (5.018 × 

10-4 * SC3

) - (2.466 × 10-2 * BP3) - (1.029 × 10-2 * CD4) 

0.640 0.974 0.011 0.105 

Complete 

METH 

CONC 

-24.505 - (4.985 × 10-3 * AO) + (6.376 * CD) + (0.110 * SC) - 

(4.021 × 10-2 * DTG) - (4.395 × 10-3 * PW) + (3.212 × 10-3 * PL) 

+ (0.831 * BP) - (7.966 × 10-3 * EV) - (0.879 * CD2

) - (3.125 × 

10-3 * SC2

) - (3.487 × 10-2 * BP2) + (3.986 × 10-2 * CD3) 

0.678 0.862 0.009 0.096 

The equations in Table 2 for GGV production rate 

and methane concentration from active panels show 

both of these quantities are functions of panel 

completion (PC), panel length (PL), panel width 

(PW) and exhauster vacuum (EV). The parameters 

PL, PW and PC are related to the volume of the gob 

which also is an indication of the available gas 

amount at a certain stage of mining. Casing diameter 

and the exhauster vacuum, on the other hand, are 

more related to the transmission of gas to the surface 

or borehole productivity. However for methane 

concentration, distance of the slotted casing to the top 

of the mined coal bed (SC) and the distance of the 

GGV to tailgate were found to be influential, instead 

of casing diameter. If the borehole is too close to the 

coal bed in an active mine, the gas is diluted with 

mine air. At higher (second and third) orders of 

influence, percent of panel completed (PC), the 

distance of the GGV from panel start (DF), and 

casing diameter (CD) were found to affect GGV gas 

production rate. The variables SC and DTG were 

found to impact methane concentration. 

For completed, and possibly sealed, panels the 

equations and the influencing parameters were 

different due to the previously discussed nature of the 

gob gas reservoir after panel completion and how this 

reservoir may interact with the outer boundary 

conditions, such as the GGV and the surrounding 

atmosphere. Since the panel is completed, the inner 

moving boundary (PC) is naturally not effective 

anymore. The volume of gob and the gas availability 

is fixed by PL and PW with gas availability usually 

showing a declining behavior. Since the panel may 

not be ventilated anymore, the existing pressure 

differential that affects the gas flow seems to be due 

to interplay between barometric pressure (BP), 

exhauster vacuum applied to the wellhead of the 

GGV (EV), and the average overburden to the panel 

(AO). Average overburden is particularly important 

for the gas production from completed panels for its 

effect on the BP at the mine level and the energy 

required, EV, to bring the gas up to the surface. Thus, 

the difference between BP and EV at a certain depth 

affected by the length of the borehole (AO) is 

important for gas production rate and methane 

concentration. 

For completed panels, casing diameter (CD) and the 

distance of slotted casing to coal bed (SC) are both 

important for gas production rate and methane 

7
concentration. Casing diameter (CD) impacts the gas 

production by creating a larger sink. It is also 

important for methane concentration as it captures 

more of the methane rich gas from the completed 

panel. For both gas production rate and methane 

concentration, SC seems to be important for the 

borehole to draw easily in a high permeability zone 

for higher gas production. This is different than 

active panels where SC was not found to be 

particularly important for GGV gas production from 

active panels. The parameters discussed here and in 

the previous paragraph were also influential at higher 

orders in the equations of GGV gas production and 

methane concentration for completed panels. 

5. Conclusions 

Degasification of a mine is a high priority when 

mining any gassy coal seam. Using data from MCP 

to determine models and relationships between 

variables we have found the following: 

• MCP predicts the GGV methane 

concentration and production, but does not 

define relationships between the variables. 

• Linear modeling, while more easily 

accomplished with a simpler model, does 

not fit the data adequately. 

• Non-linear modeling was then used and was 

determined that a 3rd or 4th degree 

polynomial was a best fit. In reality, they 

were all allowed to fit 4th degree 

polynomials, but the coefficients of the 

highest order terms were either zero or very 

small. Therefore, those terms were 

eliminated, which reduced the order in the 

final equations. 

• The non-linear approach made the model 

equations more complicated than the linear 

approach. 

• The non-linear regression created a model 

that better honored the relationship between 

the variables that affected the output of 

GGV’s. 

6. References 

EIA, Energy Information Administration. 1995. 

Longwall mining. U.S. Department of Energy, Office 

of Coal, Nuclear, Electric and Alternate Fuels, Report 

No: DOE/EIA-TR-0588, Washington, D.C. 

Karacan, CÖ, Diamond, WP, Esterhuizen, GS, 

Schatzel, SJ. 2005. Numerical analysis of the impact 

of longwall panel width on methane emissions and 

performance of gob gas ventholes. 2005 International 

Coalbed Methane Symposium, 17-19 May, 

Tuscaloosa, Alabama. 

Karacan, CÖ. Esterhuizen, GS, Schatzel, S, 

Diamond, WP. 2007. Reservoir-simulation based 

modeling for characterizing longwall methane 

emissions and gob gas venthole production” 

International Journal of Coal Geology. 71 (2-3), 225-

245. 

Karacan, CÖ. 2008a. Modeling and prediction of 

ventilation methane emissions of U.S. longwall 

mines using supervised artificial neural networks. 

International Journal of Coal Geology. 73, 371-387. 

Karacan, CÖ. 2009a. Elastic and shear moduli of 

coal-measure rocks derived from basic well logs 

using fractal statistics and radial basis function 

networks International Journal of Rock Mechanics 

and Mining Sciences. 2009a. 

Karacan, CÖ. 2009b. Degasification system selection 

for U.S. longwall mines using an expert classification 

system. Computers and Geosciences. 35, 515-526. 

Karacan, CÖ. 2009c. Forecasting gob gas venthole 

production performances using intelligent computing 

methods for optimum methane control in longwall 

coal mines, International Journal of Coal Geology 79, 

131-144. 

Karacan CÖ. 2009d. Reconciling longwall gob gas 

venthole performances using multiple-rate drawdown 

well test analysis, International Journal of Coal 

Geology. 80 (3-4), 181-195. 

Karacan, CÖ. 2008b. Evaluation of the relative 

importance of coal bed reservoir parameters for 

prediction of methane inflow rates during mining of 

longwall development entries, Computers and 

Geosciences. 34 (9), 1093-1114. 

Karacan, CÖ., Goodman, G. 2009. Hydraulic 

conductivity and influencing factors in longwall 

overburden determined by using slug tests in gob gas 

ventholes. International Journal of Rock Mechanics 

and Mining Sciences. 46 (7), 1162-1174. 

Motulsky, H., Christopoulos, A., 2004. Fitting 

Models to Biological Data Using Linear and 

Nonlinear Regression: A Practical Guide to Curve 

Fitting. Oxford University Press, New York, NY, 

351pp. 

Ren, T.X., Edwards, J.S. 2002 Goaf gas modeling 

techniques to maximize methane capture from 

8
surface gob wells. In: Mine Ventilation, Euler De 

Sauza (Ed.). 279-286. 

Savin, N.E., White, K.J., 1977. The Durbin–Watson 

test for serial correlation with extreme sample sizes 

or many regressors. Econometrica 45, 1989–1996. 

Tomita, S., Deguchi, G., Matsuyama, S., Li, H., 

Kawahara, H., 2003. Development of a simulation 

program to predict gas emission based on 3D stress 

analysis. 30th International Conference of Safety in 

Mines Research Institutes, South African Institute of 

Mining and Metallurgy, 69-76. 

APPENDIX A 

- Average exhauster vacuum (EV): A vacuum applied 

to the gob gas wellhead stimulates methane migration 

into the venthole from the surrounding strata and 

prevents flow reversal into the mine. It is 

recommended to measure exhaust suction pressure at 

both below and above the flame arrestor. The average 

of these two readings can be used as the average 

vacuum applied at the wellhead as an input 

parameter. In general, higher vacuum leads to higher 

flow, but at the risk of drawing mine air into the 

GGV and diluting methane concentration. 

- Outside barometric pressure (BP): Atmospheric 

pressure is an important factor for gas emission and 

production, especially from uncovered sources, i.e., 

when the gas source is in direct communication with 

atmosphere. For uncovered gas sources (abandoned 

mines with open-to-atmosphere ventholes, landfills 

and surface coal mines), there may be a strong 

inverse relationship between barometric pressure and 

methane emissions. However, this situation can be 

very different for closed sources underground as they 

will not have direct contact with the atmosphere. 

- Casing diameter (CD): Casing diameter is used in 

calculating gob gas venthole performances. Earlier 

studies (Karacan et al., 2007; Ren and Edwards, 

2002) showed, keeping the other completion 

parameters constant, increasing gob gas venthole 

casing diameter increased cumulative methane 

production from the subsided strata. Although a 

marginal decrease in the methane concentration was 

evident from this change, possibly due to increased 

mine-air extraction with a larger sink, higher gas flow 

rates increased the overall volume of methane 

produced. 

- Distance of bottom of the slotted casing to top of 

coal bed (SC): Casing set depth with respect to the 

top of the mined coal seam may play an important 

role on amount and concentration of methane 

captured. Reservoir modeling results showed when 

the setting depth was close to or within the caved 

zone, methane concentration and total amount of 

methane captured decreased. However, one 

additional consideration for changing setting depth 

for the slotted casing may be the competency and 

productivity of the formations surrounding the slotted 

casing based on their mechanical properties and gas 

contents. Distance from the bottom of slotted casing 

to top of the coal bed should be the actual value 

reported by the venthole driller, after drilling and 

completing the venthole. 

- Distance of venthole from panel start (DF): The 

location of a gob gas venthole with respect to the 

panel start is based on the subsidence profiles 

expected during mining in order to locate the 

ventholes in tension zones. Tension zones are the 

areas within the longwall panel gob that have the 

largest openings (highest tension between strata) 

within the strata, giving a possible GGV the best 

ability to pull the gas from these fractured layers. 

The distances from the start of the panels and 

distances between ventholes along the panel are again 

based on maximizing productivity, on expected 

drainage radius of ventholes, and on emissions in the 

mines. In general, holes on the ends of the panels 

(especially the ones at the starting end) are the 

highest-quantity and longest-duration producers. This 

is attributed to enhanced mining-induced fractures on 

the ends of panels where the overburden strata are in 

tension on three sides due to support of the 

surrounding pillars instead of two sides in the holes 

along the panel. This parameter can be used to decide 

the distance of the borehole from the start of the 

panel and the distance between boreholes during 

venthole design process. 

- Distance of venthole to tailgate (DTG): The 

location of a gob gas venthole with respect to the 

panel tailgate is usually based on subsidence profiles 

expected during mining in order to locate ventholes 

in tension zones, where most fractures are open. This 

ensures maximum production from the fractured 

strata as compared to a location in the centerline of 

the panel which is in compression due to subsidence 

and re-compaction of the longwall gob. 

- Face status information (Idle/Advancing): Longwall 

production down times due to mechanical delays or 

vacation periods halt expansion of fractured methane 

reservoir which eventually may affect gas production 

rates and methane percentages from gob gas 

ventholes. 

9
 

 

10

- Linear mining advance rate (LA): Gob gas venthole 

production performance during longwall mining is 

closely related to face advance rates that affects 

fracturing and permeability due to dynamic 

deformation and subsidence. Usually, higher face 

advance rates result in lower hydraulic conductivity 

in the fractures (Karacan and Goodman, 2009), 

suggesting a possible impact on venthole 

performance. Face advance also creates an 

extensively fractured methane reservoir from which 

the gob gas ventholes can extract gas. An increased 

face advance rate causes a larger percentage of the 

panel to subside and creates a larger fractured 

reservoir in a given time compared to a slower 

advance rate. 

- Overburden depth of the gob gas venthole (AO): 

The desired locations of ventholes may be different 

than initially planned locations due to land ownership 

issues and accessibility of planned drilling locations, 

which may lead to ventholes being drilled at different 

locations (hill tops or valleys). Based on evaluating 

hydraulic properties of underground strata in field 

studies and their potential responses to longwall 

mining during these measurements, ventholes drilled 

in valleys may be more productive than those drilled 

on hill tops due to pre-existing or easily extended 

structural fractures. Also, permeability of the 

fractures at the hill-top wells during longwall mining 

is usually less compared to valley-bottom wells due 

to greater overburden depths, causing lower hydraulic 

conductivities and potentially less effective boreholes 

as opposed to shallow ventholes. This general 

information can be used as guide in deciding where 

to locate ventholes in accordance with surface terrain. 

However, keep in mind that gas content of coal 

seams generally increases with increasing 

overburden. Thus, if there are major coal seams 

within the slotted interval, information of their sitespecific gas content may lead to a better selection in 

drilling location. 

- Panel status information (Active/Completed): 

Methane concentrations and rates measured in 

venthole production are related to whether or not the 

panel has been completed. These observations 

generally indicate that ventholes start with higher 

methane concentrations that decline with time, and is 

associated with the production of available methane 

in the gob. Thus, in terms of the amount of available 

methane, information on if the panel is completed is 

important. 

- Panel length and width (PL, PW): The increasing 

size of longwall panels (width and length) usually 

affects methane emissions due to wider faces and 

larger area of fractured strata. If not captured 

effectively, additional gas emissions from a larger 

area of fractured formations can enter the mining 

environment and may create unsafe operating 

conditions. Thus, as the size of the panel increases, 

the expected increase in emissions must be controlled 

by effective degasification methods. The size of the 

panel (length and width) should be available as part 

of the mine plan. 

- Percent of the panel completed (PC): Methane 

concentration and flow rate measured in venthole 

production is also related to what extent the panel has 

been mined, since it directly affects the size of the 

gob reservoir, its limits, and methane availability. 

- Surface elevation of the venthole (SE): Surface 

elevation of a gob gas venthole and the overburden 

depth (or drilling depth) to the coal seam affects the 

atmospheric pressure at the wellhead. Both 

parameters affect the performance of a gob gas 

venthole (please also see “overburden depth of the 

gob gas venthole” and “barometric pressure”).