Advances in grid-based numerical modeling techniques for 

improving gas management in coal mines 

C.Ö. Karacan 

NIOSH Pittsburgh Research Laboratory, Pittsburgh, Pennsylvania, USA 

TX. Ren & R. Balusu 

CSIRO Exploration and Mining, Australia 

ABSTRACT: Effective gas management in coal mines, as well as proper ventilation design, is very important for 

maintaining the safety of underground coal miners. Advances in numerical modeling techniques have enabled 

evaluations of the coal mining environment using advanced grid designs and computed gas distributions through 

detailed mathematical models. These models help engineers to “see” the “unseen” areas of the mine through 

visualization of the data and ultimately help improve worker safety. This paper summarizes the modeling efforts 

conducted by NIOSH in the U.S. and CSIRO in Australia to address various gas management issues encountered 

in longwall mines. The modeling studies involved two of the most widely used grid-based techniques, namely 

computational fluid dynamics (CFD) and numerical reservoir modeling. This paper discusses the application of 

these techniques for gob inertization and gas control for spontaneous combustion, pre-mining degasification of 

coalbeds, gob gas venthole design and performance evaluation in longwalls, and gas management during 

development mining. 

* Use of the names of commercial ventilation analysis packages does not imply NIOSH’s and CSIRO’s endorsement.

1 Introduction 

In underground coal mines, mine ventilation and methane 

control systems not only provide a workable environment 

but also reduce and eliminate explosive gas mixtures. In 

this regard, the use of simulation models is beneficial in 

the initial design of a particular ventilation and gas control 

system to test different scenarios. Simulation models also 

provide flexibility in making decisions where alternative 

designs need to be evaluated as ventilation or gas control 

problems arise in the mine. 

“Classical” simulation programs, for instance 

MineVent* or Vnet* that rely on “network-type” modeling 

and solution algorithms are widely available for mine 

ventilation system design. They can be built easily, since 

they can interface directly with mine maps developed with 

AutoCAD. However, their capabilities to realistically 

model the mining environment are limited. They can be 

best used for the “network-like” regions in the mines, such 

as entries. However, these models are not capable of 

simulating what is happening in inaccessible regions, such 

as gobs, due to their 1-D and branched nature. Also, their 

information is usually limited to the pressures and flow 

rates at specific nodes or branches. Some programs, such 

as MFIRE*, can model the spread of fumes on a real time 

basis, in addition to calculating pressures, fan effects, and 

flow rates. However, these programs are becoming 

outdated due to the programming languages on which they 

were originally built (Hardy and Heasley, 2006). More 

advanced “network” programs, such as VUMA* and 

ENVIRON 2.5*, are capable of modeling additional 

parameters to assist designers in determining the 

aerodynamic and thermodynamic performance of the 

ventilation system (Biffi et al., 2006). These programs are 

also network based and are subject to similar limitations as 

their counterparts, in that they cannot model the gas control 

system as a whole. Also, it should be mentioned that all of 

these programs simulate steady state conditions and are 

only adequate for the purpose of defining performance 

parameters. They should not be used to model or analyze 

unsteady state conditions (Biffi et al., 2006). 

Grid-based numerical modeling techniques are “nonclassical” alternatives to the network-based models. They 

consider the mining environment as a volume rather than 

as one-dimensional ventilation “network” branches. 

Different mining-related geometries with varying transport 

properties can be created within the simulation volume. 

They also can model a wide range of processes, boundary 

conditions, and parameters, as well as unsteady state 

situations, which makes them advantageous compared to 

conventional approaches in ventilation design and gas 

management. 

The aim of this paper is to introduce the recent 

advances in the application of the most widely used gridbased numerical techniques, computational fluid dynamics 

(CFD) and numerical reservoir modeling, to assess gas 

control capabilities in longwall mines. An overview is 

given of the modeling efforts conducted by the National 

Institute for Occupational Safety and Health (NIOSH) in 

the U.S. and the Commonwealth Scientific and Industrial 

Research Organization (CSIRO) in Australia for various 

gas management issues encountered in longwall mines. 

The examples covered in this paper include the application 

of these techniques for gob inertization and gas 

management for spontaneous combustion control, pre­
 

 

 

 

 

mining degasification of coalbeds, gob gas venthole design 

and performance evaluation in longwalls, and gas control 

during development mining of gateroad entries. 

2 Applications of Grid-Based Numerical Modeling 

Techniques for Gas Control in Underground Coal 

Mines 

In recent years, NIOSH and CSIRO have engaged in the 

investigation of gas flow dynamics within longwall gob 

areas and development entries for improving gas capture, 

minimizing the risk of spontaneous combustion, and 

developing gob inertization and coalbed degasification 

methods to supplement the mine’s ventilation system. 

CFD models were developed by CSIRO and detailed 

reservoir models were developed by NIOSH for various 

mining and site-specific conditions. Both modeling efforts 

were integrated with field work to collect calibration and 

verification data. This section gives examples from these 

modeling studies to illustrate the capabilities of grid-based 

modeling approaches for ventilation and gas control 

research. 

2.1 ANN Modeling of Ventilation Emissions from U.S. 

Longwall Mines 

A fundamental insight into the mechanics of gob gas flow 

is essential for developing effective gas management and 

ventilation strategies. Such an understanding is also 

beneficial to the management of longwall gob gas to 

prevent spontaneous heatings (Yuan et al., 2006). 

CSIRO developed CFD models to obtain a detailed 

understanding of the gas flow mechanics and distribution 

in the caved zone of longwall gobs using actual mine 

layouts. Longwall gob permeability distributions and gas 

emissions in the gob were incorporated into the model by a 

set of user-defined functions, or subroutines that were 

written separately and attached to the main CFD solver. In 

the model, the caved longwall gob was treated as porous 

media. A typical geometry and mesh used in the longwall 

gob gas flow models are shown in Figure 1. 

Longwall Gob 

HG 

Face 

TG 

Figure 1. Typical model geometry and mesh used in the 

longwall CFD gas flow models. 

Figure 2 shows the predicted gob gas migration pattern 

from the CFD model representing the common longwall 

geometry (1000 m in length (3280 ft) × 200 m in width 

(656 ft) × 60 m (196 ft) in height) in some mines in 

Australia. In the model: 

•	 The HG side was 20 m (65.6 ft) lower in elevation 

than the TG side, 

• A U-ventilation pattern with a total airflow of 

50m3

/s (105,900 ft3/s) was used, and 

• Total gob methane emission was 600 l/s (21.2 

ft3

/s). 

The modeling results indicated that oxygen 

concentration in the gob on the headgate (HG) side was 

higher than on the tailgate (TG) side where oxygen 

concentration was up to 12% even at distances over 500 m 

(1640 ft) behind the face. Methane tends to migrate 

towards the high elevation side of the gob close to the 

tailgate. The layering of gob gas was also observed using 

similar models in which the ventilation air penetrated along 

the lower sections of the gob, particularly immediately 

behind the face and further along the HG side. 

TG 

HG 

200 m (656 ft) 

O2 level % 

Figure 2. Plan view of oxygen concentrations in the gob 

(along a horizontal section in the middle depth). 

A similar problem was investigated by NIOSH using a 

reservoir modeling approach. A typical reservoir grid 

representing the mining environment is shown in Figure 3. 

As in the case of the CSIRO/CFD modeling study, 

determining and inputting permeability changes in the gob 

as a result of mining were key elements of this model. 

This was achieved using FLAC (Fast Lagrangian Analysis 

of Continua) modeling of the area and converting stresses 

to permeabilities (Esterhuizen and Karacan, 2007). The 

reservoir model was different from the CFD model, in that 

permeability values could be directly entered into the 

model as an array without the need for writing separate 

user-defined functions. 

The aim of this study was to model air leakage into the 

gob, to investigate the effect of gob gas ventholes drilled to 

different depths on gob gas flow, and to determine the 

movement of released tracer gas (Esterhuizen and Karacan, 

2007). Figure 4 shows the effect of a gob gas venthole 

drilled too deeply (<9 m (35 ft) into the gob. This figure 

shows that the presence of a pressure sink due to the 

borehole changes flow circulation in the gob and thus 

allows the borehole to produce ventilation air from the gob. 
Location of 

borehole 

 

 

 

Figure 3. A three-dimensional grid model of the gob and 

the mining layer. The top layers were cropped for length 

and width to help visualize the modeled mining layer. 

Gob 

(Caved Zone) 

Pillars 

Entry 

Coalbed Layer 

Unmined 

Coalbed Longwall 

Face 

Figure 4. Influence of a gob gas venthole penetrating into the caved zone. A: A plan view of the lower part of 

the caved zone near tailgate entries. B: A vertical cross-section across the gob gas venthole location. 

 

Tailgate 

Overlying Pillar strata around the 

panel 

Headgate 

Gob section shown in the plan view 

A 

Pillar 

B 

Gob gas venthole 

Tailgate

2.2 Modeling Gas Control in Longwall Gobs Using “Gob 

Gas Ventholes” 

A particular application of grid-based modeling related to 

longwall mining is the optimization of surface gob gas 

venthole designs with the objective of maximizing gas 

production. This modeling can reduce the emissions into 

the mine workings as well as minimize the risk of 

spontaneous combustion. 

CFD was used to model in combination with field 

monitoring and trials to optimize gob drainage design 

parameters. Figure 5 shows the predicted oxygen 

concentration patterns in the gob with the use of surface 

gob gas ventholes drilled on both headgate and tailgate 

sides. Results indicate that gas removal from gob gas 

ventholes would substantially improve the overall 

performance of the gas drainage system and reduce the size 

of methane-rich area on the tailgate side as well as the gas 

concentrations (in comparison to Figure 2). 

This optimum gob gas drainage strategy helps in 

reducing gob gas migration towards the face and also helps 

in reducing the sharp gas emission fluctuations associated 

with changes in barometric pressure. These strategies, 

together with a set of guidelines for optimum gob drainage 

strategies, have been successfully implemented at several 

Australian coal mines (Balusu et al., 2002). 

A similar problem was tackled by NIOSH using 

numerical reservoir simulation. In that study, the impacts 

on methane emissions and gob gas venthole performance 

were investigated when increasing panel width from 381 m 

(1250 ft) to 442 m (1450 ft). Also, an optimum gob gas 

venthole drilling strategy was sought to remove the excess 

gas due to this increase in panel dimensions. The modeled 

area is shown in Figure 6 (Karacan et al., 2005). 

In Figure 6, the number of vertical layers and their 

thicknesses were based on generalized stratigraphic 

sections for the mine site. The positioning of gob gas 

ventholes in the model was based on their locations on the 

study panels, and these ventholes were configured based 

on their actual reported completion data. Most of the data 

required by the reservoir simulator for this application 

were gathered from previous NIOSH publications that 

addressed general reservoir and gob gas venthole 

performance issues in this same study area. Additional sitespecific data were obtained from the cooperating mining 

company to develop reasonable assumptions for setting the 

initial (pre-mining) reservoir properties of the coal and 

non-coal units. The geomechanical FLAC model was used 

to evaluate the effects of longwall mining on the 

surrounding rock mass and to calculate mining-related 

permeability changes. 
O2 level % 

HG 

TG 

200 m (656 ft) 

Figure 5. CFD applications in the development of optimum 

gob gas venthole drilling strategy. Figure shows oxygen 

distribution in the gob. 

Figure 6. 3-D grid model of the study area (inner layers 

removed) showing the major coalbeds and the sandstone 

paleochannel (a depositional feature that changes the 

thickness of or sometimes completely washes out the 

coalbed). This figure shows the actual wells used in the 

model. Colors represent different strata thicknesses. 

Gob Gas Ventholes 

Bleeder 

Air Intake and Return 

Overlying 

Coalbed -1 

Overlying 

Coalbed -2 

Sandstone Channel 

Mined 

Coalbed 

Results of this study showed that increasing the width 

of the panel did not change the productions of gob gas 

ventholes, although the emissions from the fractured strata 

did increase with width and could enter the mine’s 

ventilation system if not captured. Thus, alternative gob 

gas venthole drilling patterns were modeled (Karacan et 

al., 2005) to optimize the capture of this gas. Figure 7 

shows the cumulative methane productions that could be 

obtained with two different borehole patterns shown in the 

inset figure. Different colored curves in Figure 7 

correspond to the cumulative productions that could be 

achieved with each of these borehole patterns. The results 

showed that the scenario with gob gas ventholes on either 

side of the panel, as in the CFD model, achieved the 

highest venthole production, which reduced emissions to 

the level seen before panel size was extended. This should 

result in a safer mining environment. 

Figure 7. Comparison of cumulative gob gas venthole 

methane production from the 381-m (1250-ft) and 442-m 

(1450-ft) panels with various well configurations. An 

actual borehole is the one drilled and operated in the 

modeled area. An optimum borehole is the one drilled 90 

m (300 ft) from the tailgate to 10-12 m (40-45 ft) to the 

tope of the coalbed and operated continuously. Cumulative Methane (MMSCF) 250 

200 

150 

100 

50 

0 

0 50 100 150 200 250 300 

Time (days) 

Base Cases 

1450-ft 

1450-ft 

Optimal wells 

Actual wells 

Mining direction 

Tailgate 

1250-ft 

Headgate 

1450-ft 

2.3 Simulations for Spontaneous Heating Control and Gob 

Inertization 

Control of spontaneous combustion in a longwall gob is 

critical safety and optimum production of in some of the 

mines in Australia and in the western US. CFD simulations 

have been used to investigate different ventilation layouts 

and practices with respect to the likelihood of spontaneous 

combustion for Australian longwall mines. Results from 

the CFD studies have helped improve ventilation designs 

and practices to minimize the onset of heating problems. 

These models investigated the spontaneous heating and 

combustion risks by evaluating gas distributions in the 

gobs and by recommending different ventilation schemes 

for various methane emission rates in the gobs. 

Yuan and Smith (2007) improved the CFD approach to 

investigate spontaneous heating problems by including 

chemical reaction and oxidation of coal in combination 

with gas distributions. They calculated the temperature rise 

due to spontaneous heating and evaluated the risk of 

combustion in the gob for various ventilation parameters 

and coal activation values. 

CFD models have also been used by CSIRO to develop 

optimum and effective strategies for inerting gobs during 

longwall sealing operations. Figure 8 shows oxygen 
 

distributions in the gob at different locations after 24 hours 

of inert gas injection. Inert gas at a rate of 0.5 m3/s (17.7 

ft3/s) was injected through the HG seal and at 200 m (656 

ft) behind the face on the headgate side respectively. The 

results indicate that the strategy of inert gas injection at 

about 200m behind the face significantly improves the gob 

inertisation process, compared with the traditional practice 

of injecting inert gas through headgate. Simulation results 

also show that other factors such as gas emission rates and 

ventilation during panel sealing would also have a 

significant influence on gob gas distribution and 

inertization. 

Figure 8. CFD simulations of gob inertization with different inertization strategies - Colors represent oxygen 

concentration. 

O2 level O2 level 

Headgate (HG) 

Tailgate (TG) 

Inert gas 

injection 

location 

Inert gas 

injection 

location 

 

In combination with field trials, optimum inertization 

strategies have been developed and implemented at an 

Australian coal mine. The gob environment became inert 

with oxygen level below 5% within a few hours of panel 

sealing (Balusu, Wendt and Xue, 2001). Further

simulations with CFD and the knowledge obtained from 

these simulations have been used in conjunction with field 

conditions to develop the optimum proactive gob

inertization schemes for two Australian coal mines.

Excellent inertization results have been achieved (Balusu 

et al., 2005) 

2.4 Modeling of Development Mining and Methane 

Control Strategies for Entry Development 

In longwall development mining of coal seams, planning, 

optimizing, and providing adequate ventilation are very 

important steps to eliminate the accumulation of explosive 

methane-air mixtures in the working environment. 

Numerical reservoir simulation was used by NIOSH for 

prediction and optimization of methane inflows and 

ventilation air requirements to maintain methane 

concentrations below statutory limits. A coalbed reservoir 

model of a three-entry development section was developed, 

assuming either the presence or absence of shielding 

boreholes around the entries against methane inflow. In the 

model, grids were dynamically controlled to simulate the 

advance of mining for parametric simulations of different 

advance rates and coalbed parameters. An artificial neural 

network approach was integrated with this model for 

prediction efficiency (Karacan, 2007b). Also, multilinear 

equations were generated for different coalbed reservoir 

parameters to predict emissions (Karacan, 2007c) 

For modeling the advance of tailgate and headgate 

entries, a three-entry development model around a 

longwall panel was studied. Figure 9 shows a snapshot of 

mining advance, pillar layout, and ventilation scheme. The 

middle entry was modeled as the “track” or haulage entry, 

where intake air was entering. The entry to the left of 

“track” was designated as the “belt” entry. The third entry 

was designated as the “return” entry, carrying away the 

majority (>90%) of the methane emissions. During 

development mining, the entries and the cross cuts 

constituting the volume to be ventilated were continuously 

extended. 

Belt 

Track 

Return Degasification

boreholes 

Pillars 

Mining 

direction 

Figure 9. A three-dimensional snapshot of the model 

during development mining. Pillars, ventilation scheme, 

and the modeled shielding wells are also shown. 

The amount and rate of methane inflow into the 

developed entries depended upon the extent of the 
continually created surfaces. This effect was simulated 

with models that ran sequentially, each characterizing an 

advance in entry development with a specified

development rate. 

Figure 10. Snapshots of mining advance, pillar layout, and the ventilation scheme in the coalbed reservoir model. 

Locations where total methane inflow is monitored are also shown. The figures show the progress of mining in the 

coalbed at the end of first and fourth section advances. 

 

T1 T4 

Monitoring points 

Belt 

Intake 

Return 

Stopping Ventilation curtain Pillar 

T1 T2 T3 T4 

A B 

Advance of entry 

development 

Coalbed properties were replaced with the assigned 

properties of the entries and the ventilation-related features 

were built. The models were calibrated with the field 

measurements of methane concentrations during entry 

development. The development of cross cuts was modeled

the same way as the entries in a dynamic fashion by

assigning corresponding reservoir properties. However, 

during each simulation run, only the last set of cross cuts 

was left fully open for ventilation airflow. During 

development of cross cuts, stoppings or block walls

(between track-belt and track-return) were automatically 

created between the restart runs to force ventilation flow 

through the last open cross cut at each section advance. A 

“curtain” resistance in the last section of the “track”

diverted some of the intake air towards the “belt” entry to 

ventilate both belt and face (Figures 10a and 10b). These

figures show the entries, cross cuts, and pillars created 

during simulations plus the paths of the ventilation airflow. 

Two different modeling approaches were used to

predict methane inflows and to estimate ventilation 

requirements during development mining. In the first

approach, these values were determined based on the 

absence of any degasification wellbores around the entries 

to shield them from migrating methane. 

The estimates of ventilation airflows that must be 

supplied to maintain specific methane concentrations are 

given in Figure 11 for varying mining rates and entry 

lengths. For these calculations, the methane inflow rates 

predicted by the simulator were used. With shorter 

development distances, only minimal adjustments to 

ventilation airflow were needed to keep methane levels 

below 1%. 

Such adjustments appeared to be independent of

mining rate. With longer development distances, more 

ventilation airflow is needed for constant methane levels 

and the amount of airflow was more strongly affected by 

the mining rate. If the ventilation alone cannot 

limit methane levels, decreasing the mining rate may be an 

effective, but not necessarily, a desired control (Karacan, 

2007b). 

0 

20000 

40000 

60000 

80000 

100000 

120000 

140000 

160000 

0 50 100 150 200 

Entry Development Rate (ft/day) 

Air Flow Rate (cfm) 

2000 ft Entry-0.5% CH4 

2000 ft Entry- 0.75% CH4 

2000 ft Entry-1.0% CH4 

12000 ft Entry-0.5% CH4 

12000 ft Entry-0.75% CH4 

12000 ft Entry-1.0% CH4 

Figure 11. Calculated airflow rates required to maintain 

various methane concentrations for different mining rates 

and entry lengths. 

A similar modeling approach was used to evaluate the 

effect of various coalbed reservoir parameters on methane 

emissions in development mining (Karacan, 2007c). The 

results of the simulations were used to generate simple 

predictive equations using multilinear regression analysis 

for a 15.3 m/day (50 ft/day) section advance rate. Table 1 

shows the equations obtained using this approach. 

Table 1. Linear models for predicting methane inflow 

rates into development entries of various lengths using key 

coalbed reservoir parameters (Thick: Thickness (in ft), 

Pres: Pressure (in psi), S. Time: Sorption time constant (in 

days), Ky: Butt cleat permeability in this study, but can be 

interpreted as the cleat permeability perpendicular to 
 

 

 

 

entries (in milli Darcies), L. Vol: Langmuir volume 

(scf/ton), L. Pres: Langmuir pressure (in psi), W. Sat: 

Water saturation (in percent)). Reservoir parameters were 

determined using ANOVA and backward-elimination 

methods. 

Dev 

length 

(ft) 

Methane inflow rate (scf/day) = 

2000 -9025 0349 + (18676 8392 × Thick) + (1367 1790 × 

Pres) + (-581 1950 × S Time) + (13100 6770 × Ky) 

+ (121 6918 × L Vol ) + (-197 1251 × L Pres ) + 

(-1280 3404 × W Sat ) 

6000 -193021 3700 + (49697 1335 × Thick) + (2734 4987 

× Pres) + (-997 3667 × S Time)+ (19103 2963 × Ky) 

+ (298 3296 × L Vol ) + (-544 3628 × L Pres ) 

10000 -256242 0600 + (73838 2649 × Thick) + (3764 7566 

× Pres) + (-1256 0067 × S Time)+ (21052 8924 × 

Ky) + (457 1441 × L Vol ) + (-867 7456 × L Pres ) 

12000 -283185 5900 + (85055 0763 × Thick) + (4219 7519 

× Pres) + (-1357 3077 × S Time)+ (21403 8583 × 

Ky) + (532 4142 × L Vol ) + (-1023 3603 × L Pres ) 

The models presented in Table 1 do not contain all of 

the initially selected coalbed parameters. For instance, the 

model for the 610-m (2000-ft) case does not contain 

porosity, irreducible water saturation, end point relative 

permeability to gas, and face cleat permeability. Due to 

their low significance, they were eliminated from the set of 

independents during the backward-elimination process. It 

may be surprising to see that face cleat permeability is also 

not included in the equations for predicting methane inflow 

during development mining simulated in this study. This is 

because for this study the entries advance parallel to the 

face cleats, which makes butt-cleat permeability more 

significant for controlling methane inflows. For longer 

development distances, water saturation is also eliminated 

from the independent variables, since its significance on 

methane flow decreases for long entries. 

Table 1. Linear models for predicting methane inflow 

rates into development entries of various lengths using key 

coalbed reservoir parameters (Thick: Thickness (in ft), 

Pres: Pressure (in psi), S. Time: Sorption time constant (in 

days), Ky: Butt cleat permeability in this study, but can be 

interpreted as the cleat permeability perpendicular to 

entries (in milli Darcies), L. Vol: Langmuir volume 

(scf/ton), L. Pres: Langmuir pressure (in psi), W. Sat: 

Water saturation (in percent)). Reservoir parameters were 

determined using ANOVA and backward-elimination 

methods. 

3 Conclusions 

This paper is a brief summary of advances in grid-based 

modeling that were developed by NIOSH and CSIRO to 

address gas management problems in underground coal 

mines. The results show that CFD and reservoir 

simulations can complement each other and, in most cases, 

be suitable, although more complicated alternatives to 

classical network ventilation models. This is especially true 

in situations where network models are neither suitable nor 

capable of solving the problem and where grid-based 

models are highly effective in modeling the mining 

environments and different mining processes. The 

visualization interfaces allow engineers to “see” the 

“unseen” areas of the mine, such as gobs. 

The models presented in this paper have greatly 

improved the fundamental knowledge of gob gas flow 

patterns and gas distribution under the influence of various 

geological and mining parameters. The significant insights 

gained from these studies have helped to improve in the 

development of control measures and practices through the 

effective use of gob gas ventholes, spontaneous heating 

management, gob inertization strategies, and methane 

control in development mining. 

References 

Balusu R., Xue S. Wendt M., Worrall R. and Mallett C. 

2002. Mine gas control, Exploration and Mining 

Report No 1012C. 

Balusu R., Wendt M. and Xue S. 2001. Optimisation of 

inertisation practice. ACARP Project C9006, 

Exploration and Mining Report No 907F. 

Balusu R., Ren TX., and Humphries P. 2005. Proactive 

inertisation strategies and technology development. 

ACARP Project C12020 Exploration and Mining 

report No 907F. 

Biffi M., Stanton D., Rose H. and Pienaar D. 2006. 

Ventilation strategies to meet future needs of the 

South African platinum industry, International 

Platinum Conference, The Southern African Institute 

of Mining and Metallurgy, Sun City. 

Esterhuizen G.S. and Karacan C.Ö. 2007. A methodology 

for determining gob permeability distributions and its 

application to reservoir modeling of coal mine 

longwalls. 2007 SME Annual Meeting, Denver, CO. 

Hardy R.J. and Heasley K.A. 2006. Ventilation simulation 

programs MineVent and MFIRE: Updates to advance 

the technology of simulation programming. 11th 

U.S./North American Mine Ventilation Conference, 

State College, PA. 

Karacan C.Ö., Diamond W.P., Esterheuizen G.S., Schatzel 

S. 2005. Numerical analysis of the impact of longwall 

panel width on methane emissions and performance of 

gob gas ventholes. Proc. 2005 International Coalbed 

Methane Symposium, Paper 0505. Tuscaloosa, 

Alabama. 

Karacan C.Ö., Esterheuizen G.S., Schatzel S.J., Diamond 

W.P. 2007a. Reservoir simulation-based modeling for 

characterizing longwall methane emissions and gob 

gas venthole production. International Journal of 

Coal Geology 71, 225-245. 
 

Karacan C.Ö. 2007b. Development and application of 

reservoir models and artificial neural networks for 

optimizing ventilation air requirements in 

development mining of coal seams. International 

Journal of Coal Geology 72, 221-239.

Karacan C.Ö. 2007c. Evaluation of the relative importance 

of coalbed reservoir parameters for prediction of 

methane inflow rates during mining of longwall 

development entries. Computers and Geosciences (in 

press). 

Yuan L., Smith A.C. and Brune J.F. 2006. Computational 

fluid dynamics study on the ventilation flow paths in 

longwall gobs, 11th U.S./North American Mine 

Ventilation Conference, State College, PA. 

Yuan L. and Smith A.C. 2007. Computational fluid 

dynamics modeling of spontaneous heating in 

longwall gob areas. 2007 SME Annual Meeting, 

Denver, CO.