1. INTRODUCTION 

Underground longwall mining of coal causes large 

scale disturbance of the surrounding rock mass. 

Stress changes and mining induced fracturing of the 

rock can increase the permeability and can liberate 

methane from the surrounding strata. The influx of 

methane can be a significant hazard in longwalling 

operations and extensive methane mitigation 

techniques are employed by coal mine operators. 

However, explosions have been the cause of nine 

out of eleven coal mining disasters that occurred 

since 1980 in the USA, [1]. 

The continued safe extraction of coal by the 

longwall method requires that methane control 

methods be both cost effective and reliable. Current 

methane control methods have been developed 

largely by field experimentation and trial and error 

techniques. Nevertheless, on-line monitoring 

systems are still required that shut down the 

longwall operations when methane levels exceed a 

critical value. The difficulty of predicting methane 

inflow is a significant barrier to designing and 

optimizing control measures. 

Advanced numerical models present an opportunity 

to realistically simulate rock mass response to 

longwall operations and the associated methane 

liberation and flow through the fractured rock mass 

without resorting to field experimentation. This 

paper describes the recent application of 

geomechanical as well as reservoir modeling 

techniques to investigate the effect of longwall 

mining on the permeability of the surrounding strata 

and methane liberation and flow from the 

surrounding rocks towards the mine workings. The 

work is largely based on field data and methane 

 

 

Development of Numerical Models to Investigate 

Permeability Changes and Gas Emission around Longwall Mining Panel 

Esterhuizen, G.S. 

NIOSH, Pittsburgh Research Laboratory, Pittsburgh, Pennsylvania, USA 

Karacan, C.O. 

NIOSH, Pittsburgh Research Laboratory, Pittsburgh, Pennsylvania, USA 

 

 

ABSTRACT: Underground longwall mining of coal causes large scale disturbance of the surrounding rock mass. The disturbance 

can increase the rock mass permeability through a reduction on the stress as well as formation of new fractures in the rock. 

Methane gas contained in the disturbed rock mass can migrate towards the low pressure mine workings and present an explosion 

hazard. This paper describes the application of a finite difference program to develop a geomechanical model that predicts 

permeability changes within the rock mass. The calculated permeabilities are used as input to a reservoir simulator that models 

methane desorption from the coal matrix, methane release from the rock layers and flow towards the mine excavations. The model

also considers the basic characteristics of the mine ventilation network. 

The geomechanical model uses empirical relationships between fracture permeability and stress to calculate permeability changes

around a longwall face. The extent of rock failure is determined using a strain softening model that considers both rock matrix and 

bedding plane failure. The cave rock (gob) is modeled as a compressible, granulated material. The calculated horizontal and 

vertical permeabilities around the longwall face are averaged and used as one of the inputs to the reservoir model. The reservoir 

model was developed and calibrated against records of methane flow at a study mine in southwestern Pennsylvania. Good 

correlation between actual gas production and model outputs has been achieved. The modeling approach provides a basis for 

estimating methane inflow and optimizing control measures. 
control techniques employed in the coal mines of 

the Eastern United States. 

A two staged approach has been followed to 

develop models of methane emissions and flow 

around longwall mines. The first stage has been to 

make use of the FLAC2D [2] finite difference code 

to simulate the geomechanical response of the rock 

mass to longwall mining. The program was used to 

calculate the stress changes, extent of rock 

fracturing and bedding plane shear. The output of 

the FLAC models was used to calculate likely 

permeability changes, based on empirical 

relationships. The permeability distribution was 

then used to develop inputs for the 3-D 

compositional reservoir simulator (GEM) by 

Computer Modeling Group [3]. The reservoir 

simulator was used to develop a model of several 

longwall panels in a study mine against which the 

model was calibrated by history matching of gob 

vent borehole (GVB) methane production. The 

model has been used to evaluate the relative merits 

of methane drainage options. 

2. PERMEABILITY OF COAL MEASURE 

ROCKS 

Coal measure rocks typically consist of interbedded 

shale, siltstone, sandstone, claystone and limestone. 

The permeability is anisotropic because of 

interlayered low and high permeability strata [4]. 

Sandstone and limestone beds have the highest 

permeabilities and act as aquifers, they promote 

horizontal flow and have relatively high storage 

capacity. The shales act as aquitards, having low 

permeability, but may contain fractures and bedding 

planes that enhance permeability. Thin clay bands 

can exist within the measures that act as aquicludes. 

Coal is highly transmissive, but typically has poor 

storage. The overall permeability of the strata tends 

to decrease with depth, a decrease of 1 order of 

magnitude of the permeability of coal for every 

25 MPa increase in overburden loading was 

reported by Sparks [5]. 

Permeabilities measured in the field can be 

dominated by fracture flow and are typically highly 

variable. Tests are usually performed in vertical 

wells, which provide a better indication of 

horizontal permeability than vertical. Test results 

published by Hasenfus et al. [6] in strata above the 

Pittsburgh coalbed showed that the permeability can 

vary by several orders of magnitude in different 

sections of a vertical borehole. They measured 

hydraulic conductivties of 7x10-6cm/s in sandstone 

near the ground surface. Brutcher et al. [7] tested 

the conductivity of a sandstone aquifer and found 

the values to vary between 10-4 and 10-6 cm/s while 

shale conductivity was one order of magnitude 

lower. Booth and Spande [8] reported hydraulic 

conductivities of 9x10-5 cm/s for sandstone in 

Southern Illinois. Matetic et al. [9] reported 

permeabilities of 7x10-6 cm/s in shale materials near 

surface and 7x10-5 cm/s for sandstone in 

southeastern Ohio. 

The field measured hydraulic conductivities all fall 

within published ranges for sandstone and shales 

and the upper limits fall in the range that one would 

expect for jointed rock. Coal measure rocks in the 

eastern United States are typically poorly jointed, 

but contain bedding planes that act as 

discontinuities which allow horizontal flow. 

Vertical flow is constrained, especially by thin clay 

layers. 

3. EFFECT OF LONGWALL MINING ON 

PERMEABILITY 

Longwall mining induces both stress increases and 

stress reductions in the surrounding rock. Around 

the edges of the panel, the stresses will increase, 

while the rock directly above and below the 

extracted panel will experience significant stress 

relief. In addition, the increased stresses can cause 

fracturing of the rock mass. These changes can 

have a profound effect on the rock mass 

permeability. Field studies have shown both 

increasing and decreasing changes of approximately 

one order of magnitude in the hydraulic 

conductivity of the rock mass above a longwall 

panel in Pennsylvania [6]. 

3.1. The effect of stress changes 

Changes in stress can produce large variations in the 

permeability of laboratory and field scale rock. In 

the laboratory, the permeability will initially 

decrease as rock is subject to increasing loads, but 

the permeability will increase as the rock reaches its 

peak strength, and permeability attains a maximum 

during the post failure stage [10]. The permeability 

of the field scale rock mass is affected by the 

closure or opening of fractures under changing 

stresses. The equivalent permeability can be related 

to the fracture aperture and fracture spacing [11, 12, 

13]. Some researchers have related changes in 

permeability directly to changes in the confining 
stress, and found an exponential relationship 

between permeability and stress [14]. 

3.2. The effect of fracturing 

In addition to stress changes, fracturing occurs in 

the rock mass in the vicinity of a longwall panel. 

Three zones can be distinguished in the roof rocks, 

shown in figure 1: the caved zone, fractured zone 

and the bending zone [15, 16]. The caved zone is 

created as the mining face advances and the 

immediate overburden falls and fills the void 

created by the extraction of the coal. The caved 

zone extends upwards, 3-6 times the extraction 

thickness. The caved zone is characterized by 

irregular rock fragments that may have rotated 

relative to their initial locations, resulting in 

relatively high void ratios and permeability. 

Laboratory tests have shown that the void ratio can 

be in the order of 30%-45% [17]. As the face 

advances, the caved rock (gob) is re-compacted by 

the weight of the overburden. The amount of recompaction depends on the depth of overburden and 

the strength of the gob material. The permeability of 

the caved zone can be expected to be high but will 

vary as the compaction of the gob varies. 

The fractured zone is located above and around the 

caved zone and is characterized by near vertical 

fractures and bedding plane shearing caused by the 

passage of the longwall face [6]. Bed separation 

can occur in this zone. The fractured zone can 

extend 30 to 60 times the extraction thickness. In 

this zone, water drains directly to the caved zone 

and into the mine workings. Measurements of 

permeability in the fractured rock have shown up to 

forty fold increases in permeability [18]. 

Above the fractured zone is the bending zone. The 

rock is essentially un-fractured, but can experience 

shearing along bedding planes as they are deflected 

over the edges of the extracted longwall panel [6]. 

Bedding plane shear will affect the horizontal 

conductivity of the rock. Field observations have 

shown that water levels and ground movements 

occur up to 60 m (200 ft) ahead of an advancing 

longwall face. These movements can be associated 

with shear along weak clay filled bedding planes. 

Rock mass disturbance also extends into the floor of 

a longwall panel. The floor experiences stress relief 

as the longwall face passes overhead. The stress 

relief will be partially reversed as the gob is recompacted by the weight of the overburden. In 

addition, the elevated abutment stresses can cause 

deep seated fracturing of the floor rocks at the 

advancing face and around the stationary 

abutments. Floor gas emissions are not uncommon 

in longwall mines and can be explained by the 

failure and stress relief in the floor. 

4. METHANE SOURCES AND CONTROL 

The sources of methane in longwall operations are 

likely to be: the coalbed being mined, overlying or 

underlying coal coalbeds and to a lesser extent the 

methane contained within the surrounding rock 

strata. Porous rocks such as sandstones are likely 

candidates for gas storage. However, Diamond et 

al, [19] reported that the primary sources of 

longwall gob gas resulting from the mining of the 

lower Kittanning were the coalbeds in the overlying 

strata. As much as 91% of the longwall gob gas 

originated in the overlying coalbeds, with those as 

high as 200 ft above the mined coalbed contributing 

gas to the gob.. 

Prior to mining, the gas and groundwater are in 

equilibrium and are contained by various clay rich 

layers. The disturbance caused by the longwall 

mining first dewaters the rock within the caved and 

fractured zones [4], this is followed by gas 

liberation mainly from the coalbeds, as the pore 

pressure drops. The stress relief and mining related 

fracturing can provide new pathways for the flow of 

methane to the mined excavations. Material 

balance calculations indicated that the volume of 

the gas drained from the strata directly overlying the 

longwall panel could only account for 40% of the 

total volume of gas vented by the ventilation system 

and gob vent boreholes [19]. The rest of the 

methane migrates from the adjacent formations 

Bending

zone

Fractured

zone

zone

Caved

Complete

Partial

Mining induced

fracture Bedding plane separation

Bending

zone

Fractured

zone

zone

Caved

Complete

Partial

Mining induced

fracture Bedding plane separation

Fig. 1. Schematic vertical section showing strata 

movement above a longwall panel [16]. 

Longwall Panel 
towards the low pressure fractured zone. 

Furthermore, the high permeability caved zone acts 

as a conduit for methane by providing a connection 

between the face area and the overlying fractured 

zone. 

Methane control methods can include pre-drainage 

of methane ahead of the longwall, increased 

ventilation to dilute the methane, a bleeder 

ventilation system in which exhaust shafts are 

dedicated to methane extraction and the extraction 

of methane from the overburden above a longwall 

panel through the use of GVB’s. Gob vent 

boreholes are drilled above the panel to capture the 

gas from subsided and relaxed strata before it enters 

the mining environment. Most GVB’s are drilled 

within a short distance (10-30 m) of the coalbed 

being mined and cased with steel pipe. Commonly, 

the bottom section of the pipe is slotted and placed 

within the gas production zone, where extensive 

fracturing occurs as the overburden caves into the 

unsupported mine void and establishes a 

preferential pathway for the released gas towards 

the GVB [20]. Issues such as the optimal location 

of GVB’s relative to the longwall panel edges, and 

the spacing between GVB’s, the correct depth of 

slotted casing and operational practices have all 

been based on trial and error methods. Optimal 

operation of GVB’s and the mine ventilation system 

requires that a balance be found between the 

pressures and methane flow rates in the GVB 

system and the bleeder system [21]. Sub-optimal 

operation of the GVB system will result in 

excessive methane flow to the workings, while the 

converse will cause mine air to be drawn towards 

the GVB’s. 

5. GEOMECHANICAL MODELING 

The geomechanical and reservoir model studies 

were carried out using data from a study mine in 

Pennsylvania. 

5.1. Geology and Mine Layout 

The study mine operates in the Northern 

Appalachian section of the Pittsburgh Coalbed in 

Greene County, Southwestern Pennsylvania, USA. 

In the area, overburden depths ranged between 152 

and 274 m (500 and 900 ft). Longwall panels in the 

primary study area were initially 253 m (830 ft) 

wide and were increased to 305 m (1,000 ft). The 

panels are super critical, i.e. caving and subsidence 

of the overburden is fully developed, and an 

increase in the panel width does not result in an 

increase in the maximum surface subsidence. 

A generalized stratigraphic section of the strata 

above the Pittsburgh Coalbed in the study area is 

shown in Figure 2. Several coalbeds with a 

combined thickness of almost 3 m (10 ft) are 

present in the 26 m (85 ft) of strata immediately 

above the Pittsburgh Coalbed, and thought to be the 

primary source of strata gas in the area. Within this 

interval, the thickest coalbed is the Sewickley 

Coalbed, which is about 25 m (75 ft) above the 

Pittsburgh coalbed and the Waynesburg coalbed 

about 70 m (230 ft) above the Sewickley. Also 

important from a geomechanical point of view is the 

presence of substantial limestone beds in the roof of 

the Pittsburgh coalbed. The limestone is stiffer and 

stronger than the surrounding shale, claystone and 

siltstone rocks. 

5.2. Model Geometry and Mining Sequence 

FLAC 2D models were used to simulate rock 

behavior along a longitudinal section through the 

center line and a section across the width of a 

typical longwall panel at the study mine. The 

longitudinal sections were used to assess stress 

changes and rock failure around the advancing 

longwall face. The cross section was used to assess 

rock behavior around the edges of the longwall, 

which included the behavior over the chain pillars 

that are left between longwalls. 

The model dimensions were typically 400 m 

(1,300 ft) wide by 350 m (1,150 ft) deep to simulate 

a longitudinal section through a longwall panel. 

Figure 2: General stratigraphy at case study mine, showing 

variation various rock types incorporated in FLAC model. 
The models included the ground surface, the 

Pittsburgh Coalbed at 180 – 200 m (600 - 650 ft) 

below the surface and the surrounding roof and 

floor rocks. Element sizes varied, but were selected 

so that the element size was 1 m in the zone of 

interest, near the longwall face. Element sizes 

increase with increasing distance form the area of 

interest. Figure 3 shows the general layout of a 

FLAC grid indicating the main rock types. 

Mining was simulated in increments, starting from 

the left side of the grid and advancing to the right. 

Extraction of the coalbed was modeled by removing 

elements over the height of the coalbed by 2 

elements wide. The process of gob formation was 

modeled by first deleting rock elements in the roof 

of the coalbed, so that they are stress relieved, 

followed by inserting gob properties in these 

elements. Gob properties were also inserted in 

previously mined coalbed elements, so that the gob 

filled the mined void. A 2 m (6.5 ft) opening was 

maintained between the face and the gob, to 

represent the working area. The height of the gob 

was four times the mining height. 

The model was run to equilibrium at each step, so 

that rock failure and stress re-distribution would 

occur. The model included sufficient lateral extent 

of mining so that full subsidence of the overburden 

strata would occur over the mined area, representing 

super critical conditions. 

5.3. Rock Mass and Gob Parameters 

The rock mass was modeled as a strain 

softening/ubiquitous joint material, using the builtin constitutive model in FLAC. This model is well 

suited to modeling the layered coal measure rocks, 

since the bedding layers can be described as 

ubiquitous joints, while failure of the rock matrix 

can be simulated as a strain softening Coulomb 

material. Great care was taken in setting up the 

models to replicate the geological sequence with as 

much detail as practical. 

Strength data for the different rock types included in 

the models were obtained from laboratory tests [22] 

as well as field index testing using the point-load 

apparatus on core from the test mine. The mean 

and standard deviation of the intact strength as well 

as the bedding strength was determined for each 

rock type. A random sampling technique was then 

used to apply rock strengths to different layers in 

the FLAC models, each layer corresponding to a 

section of a logged core hole. 

Table 1 presents the key strength data used in the 

models. The strain softening parameters were the 

same for all the rock types. Cohesion softening was 

assumed, with a 90% drop in cohesion after 5.0 

millistrain of inelastic deformation. The friction 

Direction of mining 

Pittsburgh seam 

Sewickley seam 

Waynesburg seam

Gob 

400

350

Ground surface Symmetry plane 

Fig. 3. Longitudinal FLAC model showing layering in the 

model, the location of the main seams and the gob behind the 

advancing longwall face. 

Table 1. Mechanical Properties of Rock Strata used in Models 

Rock class Rock types Cohesion 

(MPa)

Friction 

Angle 

Poisson 

Ratio 

Elastic Modulus 

(GPa) 

Stiff soil Clay bands 0.055 21 0.25 1.5 

Very weak rock Very weak rock, claystone, drawslate 2.0 23 0.25 4.0 

Weak rock Weak rock, Black carbonaceous shale 4.5 25 0.25 6.0 

Moderate rock Moderate rock, Shale, 8.0 28 0.25 8.0 

Strong rock Sandstone 12.0 32 0.25 12.0 

Very strong rock Limestone 20.0 36 0.25 20.0 

Coal Coal 1.9 31 0.25 2.5 
angle remained unchanged after failure. The 

dilation angle was assumed to be 10o initially 

decreasing linearly to zero after 5.0 millistrain. The 

tensile strength was set at one tenth the compressive 

strength, decreasing to zero after 1.0 millistrain. 

Table 2 shows the properties used for bedding 

planes. Bedding properties were defined 

independently of the rock type since strong rocks 

can have weak bedding planes. The bedding 

properties were also assigned on a layer by layer 

basis in the models. 

The gob was modeled as a double-yield material 

which is a constitutive model in FLAC that can 

model the compaction of granular materials. Inputs 

for the gob material were determined through a trail 

and error approach by first matching the modeled 

behavior to the results of laboratory tests on 

simulated gob material, [17]. This was followed by 

further modifying the parameters so that the surface 

subsidence above the longwall panels in the models 

matched observed subsidence. The final set of 

inputs used for the gob material were as follows: 

Bulk modulus = 0.45 GPa, Shear modulus = 0.60 

GPa and Friction angle = 40°. The stress-strain 

characteristics of the gob is defined by the “cap 

pressure” table given in Table 3. 

5.4. Stress and Boundary Conditions 

The initial field stresses in the models were based 

on the approach that the rock mass is subject to a 

constant horizontal strain as a result of plate 

tectonics, [23]. A review of stress measurements in 

the Eastern United States [24] showed that the 

elastic modulus of the rocks is the main factor 

controlling the horizontal stress. For the Northern 

Appalachian region, the maximum horizontal stress 

in a rock layer can be determined by its laboratory 

scale elastic modulus and assuming a constant 

horizontal strain of about 0.5 millistrain. The 

tectonic stress is additional to horizontal stress 

caused by the Poisson effect of gravity loading. For 

the purpose of modeling, the vertical stress was 

assumed to be directly dependent on the cover load, 

the maximum horizontal stress was computed from 

the constant strain model, and the minor horizontal 

stress was assumed to be equal to the vertical stress. 

As a result of the dependence of horizontal stress on 

the elastic properties of the rock, the initial stress 

state was initialized within each rock layer 

individually. 

Model boundaries were selected so that they would 

be sufficiently far from the mining area that they 

would only have a negligible effect on the results. 

Table 2. Properties of Bedding Planes used in Models 

Bedding Plane Description Cohesion (MPa) Friction angle 

Very weak – clay fill 0.055 21 

Weak – open joint 0.5 21 

Moderate – weakly healed 3.3 24 

Strong – healed bedding plane 5.5 26 

Very strong – strongly healed bedding plane 10.0 28 

Table 3. Cap pressure table for double yield gob elements 

Strain 0.0 0.02 0.05 0.07 0.1 0.12 0.15 0.17 0.20 

Pressure (MPa) 0.0 0.1 0.3 0.6 1.25 2.25 5.00 10.00 20.00 

Table 4. Rock mass permeabilities (and hydraulic conductivities) used in models 

Rock Class Rock types 

Horizontal 

permeability 

(md) 

Horizontal 

conductivity 

(cm/s) 

Vertical 

permeability 

(md) 

Vertical 

permeability 

(cm/s) 

Soil Clay bands 0.1 9.66x10-8 0.1 9.66x10-8

Very low permeability Black Shale 0.2 1.93x10-7 0.1 9.66x10-8

Low permeability Gray shale 1.0 9.66x10-7 0.5 4.83x10-7

Coal - face cleat direction Coal 4.0 3.86x10-6 0.1 9.66x10-8

Coal – butt cleat direction Coal 1.0 9.66x10-7 0.1 9.66x10-8

Moderate permeability Limestone 2.0 1.93x10-6 2.0 1.93x10-6

High permeability Sandstone 10 9.66x10-6 10 9.66x10-6
The displacements were fixed along the bottom and 

right sides of the models, while the ground surface 

was modeled as a free surface. The left boundary 

was modeled as a symmetry plane. 

5.5. Rock Permeability and Calculation of 

Permeability Changes 

Initial permeabilities in the rock mass were based 

on field scale data from mines in the Eastern United 

States, discussed earlier, as well as other published 

sources [4, 12, 25, 26]. Table 4 summarizes the 

horizontal and vertical permeabilites used in the 

models. 

Permeability changes were calculated for both stress 

changes and rock fracturing. An exponential 

relationship was used to compute the effect of stress 

changes on rock mass permeability after the work of 

Ren & Edwards [27] and Lowndes et al. [14]. 

Permeabilities were calculated independently for 

the horizontal and vertical directions. The 

parameters were set so that a stress change of 

10 MPa would result in a change in permeability of 

about one order of magnitude. The following 

equations were used to determine the stress affected 

horizontal and vertical permeabilites respectively: 

0.25( )

0

yy yy 0 K K e h h

− σ −σ = ×

0.25( )

0 xx xx 0 K K e v v

− σ −σ = ×

where σxx and σyy are the horizontal and vertical 

stresses and the 0 subscript indicates initial 

conditions. 

The permeability of fractured rock was determined 

from published values of jointed rock and fractured 

rocks [12]. Model elements that fail in compression 

are assumed to experience an increase in 

permeability of 100 millidarcy (md) above their 

current permeability in both the horizontal and 

vertical directions, regardless of rock type. 

Similarly bedding shear failure is assumed to 

increase the permeability by 50 md in the bedding 

(horizontal) direction. The fracture and shear related 

permeability changes are added to the current 

permeabilities at the time of failure. The new 

permeability is then subject to further variation as a 

result of stress changes using logic described above. 

The built-in programming language in the FLAC 

model was used to develop a simple algorithim that 

uses the initial and current stresses as well as the 

failure state of the rock mass to calculate the 

permeability at every element. The equations were 

set up to calculate both the horizontal and vertical 

permeability. These values formed the basis for the 

permeability input to the reservoir model. Since the 

reservoir model was based on larger element sizes 

than those used in the FLAC model, the resultant 

permeabilities were averaged over corresponding 

areas to provide input to the reservoir model. 

5.6. Results of FLAC analyses 

The results of a FLAC model, showing the 

fracturing and horizontal permeability distribution 

about an advancing longwall face are shown in 

Figure 4. The distinct effect of mining related 

fracturing on permeability can be seen. It can be 

seen that the limestone below the Sewickley 

coalbed is relatively un-fractured by the mining. 

The fractured zone can be seen to be strongly 

developed above the gob and below this limestone 

layer. However, above the limestone the fracture 

zone is not contiguous, but appears to be confined 

to shearing along bedding planes in the weaker 

beds. Bedding shear can be seen to take place up to 

and above the Waynesburg coalbed. Although not 

shown in this plot, bedding shear occurs in weak 

beds above those shown in the figure. Figure 4 also 

shows the typical location of a GVB, being 

approximately 12 m (40 ft) above the mined 

coalbed. It can be seen that the GVB is well within 

the fractured zone where permeabilities are in the 

order of 50-100 md. In addition, the stresses are 

reduced in this area, resulting in an approximately 

two to five fold increase in permeability of the 

unfractured rock. The GVB would be well 

connected to any gas emanating from the Sewickley 

coalbed. It is also well connected to the highly 

permeable gob. 

Figure 5 shows a cross section across a longwall 

panel. Here the rock fracture, shearing and 

permeability distribution around a chain pillar is 

shown. It can be seen that the high stresses around 

the pillar create a low permeability zone. Rock 

fracturing creates high permeability areas around 

the edges of the pillar, and particularly down into 

the floor rocks. The strong limestone below the 

Sewickley coalbed acts as an inhibitor of the 

fracturing in the roof rocks, however, owing to 

reduced stresses, the vertical permeability in the 

limestone increases by a factor of about 2.0. A zone 

of lower permeability can be seen in and around the 

two chain pillars, with higher permeability around 

the entry between the pillars. A higher permeability 
zone exists in the roof above the gob on either side 

of the chain pillars where permeability changes are 

similar to those seen in Figure 4. The gob vent 

holes are usually targeted into this zone. It can be 

seen that the GVB would be well positioned to 

collect gas from the Sewickley coalbed. 

Scale

Permeability (log md)

0 

Fig. 4. FLAC model results of a vertical longitudinal-section through an advancing longwall face showing 

horizontal permeability contours, rock fracturing and bedding plane shear. 

Direction of mining

Gob 

Waynesburg seam

Sewickley seam

Pittsburgh seam

Typical Gob Vent Hole location

Fig. 5. FLAC model results of a vertical cross-section through a chain pillar between two longwall panels 

showing horizontal permeability contours, rock fracturing and bedding plane shear.

Scale

Permeability (log md)

0 

Gob 

Waynesburg seam 

Sewickley seam

Pittsburgh seam 

Typical Gob Vent Hole location

Gob 
6. APPLICATION OF RESERVOIR MODEL 

6.1. Model Layout and Simulation Execution 

The reservoir model was constructed using 

Computer Modeling Group’s [3] compositional 

reservoir simulator, GEM, which is an efficient, 

multidimensional, equation-of-state (EOS) 

compositional simulator that can simulate the dualporosity behavior of coalbeds for coalbed/enhanced 

coalbed methane recovery. The dual-porosity 

approach is the most widely used technique for coal 

and coal-rock composite reservoirs [28, 29, 30]. In 

this formulation, fracture-fracture, and matrixfracture transfers are allowed. In order to handle 

fluid compositions and sorption/desorption time 

delays, compositional non-equilibrium models are 

generally used for coal layers. A detailed 

description of the reservoir modeling approach is 

presented in Karacan et al. [31] 

Figure 6 shows the 3-D grid model that was 

constructed of the study mine. In the grid model, the 

top-most layer of the model was assigned as the 

Waynesburg Coalbed, and the bottom-most layer 

was dedicated to the Pittsburgh Coalbed, or mining 

layer. The other lithologies were represented in the 

grid model based on their thicknesses and 

sequences as a function of depth. For convenience 

in generating the grids, the layers were assumed to 

be uniform in thickness and continuous throughout 

the field. The number of vertical layers and their 

thicknesses were based on the generalized 

stratigraphy of the area. 

In the model, the locations of GVB’s were 

determined based on their locations on the panels. 

Their completions were modeled based on their 

actual completion data. At this mine site the GVB’s 

were drilled to within 12-13 m (40-45 ft) of the top 

of the Pittsburgh Coalbed, and the 17.8-cm (7-in) 

casing, with 61 m (200 ft) of slotted pipe on the 

bottom, was installed. 

Restart models were constructed to model the 

advancing longwall as a moving boundary problem. 

These models were run sequentially, each 

characterizing a mining stage and its related strata 

disturbance. In this process, the simulation outputs 

from the previous model were written in a “restart” 

file, the next model used this file as the initial 

condition and updated the longwall face and 

reservoir changes due to the geomechanical 

disturbance resulting from the next face position. At 

this stage, the averaged permeabilities calculated by 

Pittsburgh seam 

Sewickley seam 

Waynesburg seam 

Fig. 6. The 3-D grid model representing the stratigraphic section on the right and the locations of GVB’s. 
the FLAC models were incorporated into the model 

to simulate fracturing of the strata and permeability 

changes due to mining. 

6.2. Example of Results/Calibration 

Model calibration was performed through history 

matching of the model outputs to the observed 

production of the gob wells. In this study mainly, 

disturbed/undisturbed permeability values, a coal 

desorption time constant, and the permeability of 

the caved zone were used for matching the gas flow 

rates and gas compositions from gob gas ventholes. 

Then, the simulated gas rate and concentrations 

from each GVB were compared with the actual 

data. Based on the comparison between simulated 

and actual productions for the entire period of 

simulation, adjustments were made to the unknown 

parameters, and additional runs were completed 

until an acceptable agreement was judged to be 

made. During the history match runs, while 

adjustments were being made to the unknowns, 

GVB’s were operated with the targeted gas rates 

that were the observed daily rates given on weekly 

basis as the well control constraint, and wellbore 

pressures were computed by the simulator. 

However, the acceptance criteria for the calculated 

pressures were that they should be lower than and 

close to atmospheric pressures, as they normally 

should be in the field [32]. Further verification of 

the model output was carried out by comparing the 

methane concentrations from the GVB’s, calculated 

by the simulator, to the field measurements. 

Figure 7 shows the actual and simulated gas 

production rates for a total of four wells (one well 

from each panel in a four panel area) from the 

calibrated model. As can be seen from this figure, 

the rate data matches satisfactorily for most of the 

data points using the applied methodology. The 

actual and simulated methane concentrations from 

one well in each panel are presented in Figure 8. 

This shows that the methane concentrations can be 

matched for average values and general trends. 

Figure 9 shows the bottom hole pressures for the 

four wells, indicating that the pressures remain at or 

near atmospheric values after the well was 

intercepted and became operational, as observed in 

the field. This approach has been successful for 

calibration of the model and gives some degree of 

confidence to the predictive capability of the model 

developed. 

The reservoir simulator was used to study the 

source of longwall gas over the study mine. Figure 

10 shows a slab that represents the vertical section 

of the reservoir over one of the panels after the 

630th day since the start of mining and presents the 

methane concentration in the strata at that stage. 

The results indicate a high concentration of methane 

below the Waynesburg coalbed, but the flow rate in 

this area is low owing to absence of extensive 

fracturing. Closer to the mined coalbed the gas 

flow rate is higher and the gas tends to flow towards 

the GVB’s if they are in operation. The figure 

shows two GVBs and their effect in reducing 

methane concentrations below the Sewickley at that 

specific time. 

7. SUMMARY AND CONCLUSIONS 

The study has shown that the two stage approach, 

which combines the output of geomechanical 

models with reservoir models, is capable of 

realistically simulating the complex process of 

methane emission and flow around longwall panels 

in coal mines. 

The FLAC geomechanical models provide valuable 

insight into the permeability distributions around a 

longwall panel. The effect of rock fracturing as 

well as stress changes on permeability can be 

directly modeled. The permeability plots show that 

the typical GVB positions are well located to 

intersect the higher permeability zones above the 

gob. The reservoir model has been successful in 

matching actual gob gas production at the study 

mine, and can be used to evaluate methane sources 

and flow patterns in the overburden. The model 

showed that the GVB’s were effective in capturing 

gas from the overlying Sewickley coalbed at the 

study mine. 

The modeling approach provides a basis for 

evaluating methane inflow and control measures. 

This approach will result in increased safety for 

mine workers in longwall mines by improving our 

ability to predict methane inflows and enabling 

more effective design of methane control measures. 
0

200000

400000

600000

800000

1000000

1200000

1400000

0 200 400 600 800 1000

Time from the Start of Mining (Days)

Gas Rate (SCF/day)

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Gas Rate (SCF/day)

G-1 Model

G-1 Field

I-3 Model

I-3 Field

J-2 Model

J-2 Field

H-3 Model

H-3 Field

Fig. 7. Comparison of observed and simulated gas production rates from one GVB at each panel. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 200 400 600 800 1000

Time from the Start of Mining (Days)

Methane (Fraction)

G-1 Model

G-1 Field

H-3 Model

H-3 Field

I-3 Model

I-3 Field

J-2 Model

J-2 Field

Fig. 8. The comparison of observed and simulated methane concentrations from one GVB at each 

panel mined based on current longwall mining modeling approach. 
REFERENCES 

1. NIOSH. National Institute for Occupational Safety and 

Health, web page: http://www.cdc.gov/ niosh/ mining/

topics/ data/ tables/ discoal.html 

2. Itasca Consulting Group. 2000. Fast Lagrangian analysis 

of continua. 2nd Edition, Minnesota. 

3. Computer Modeling Group Ltd. 2003. Generalized 

equation of state model-GEM, User’s Guide, Calgary, 

Alberta, Canada. 

0

10

20

30

40

50

60

70

80

0 200 400 600 800 1000

Time from the Start of Mining (Days)

Well Pressure (psi)

G-1

H-3

I-3

J-2

Fig. 9. The bottom hole pressures calculated by the model for the shown GVBs of each panel. 

Waynesbur

g

Sewickley 

Pittsburgh 

Methane Mole 

Fraction 

CH4 Concentration 

depletion due to GVB 

Fig. 10: A slab that shows the vertical section of the reservoir over headgate side of a panel and the Pittsburgh 

Coalbed layer after 630th day since the start of mining. Figure shows the methane emissions (as mole fraction) 

and two of the operating GVB’s 
4. Booth, C.J. 1984. A numerical model of groundwater 

flow associated with an underground coal mine in the 

Appalachian plateau, Pennsylvania. PhD Thesis, 

University of Pennsylvania. 

5. Sparks, D.P. 1993. Coalbed gas well flow performance 

controls, Cedar Grove Area, Warrior Basin, USA. 

International Coalbed Methane Symposium, University of 

Alabama, Tuscaloosa II: 529-548. 

6. Hasenfus, G.J., K.L. Johnson and D.H.W. Su. 1988. A 

hydrogeomechanical study of overburden aquifer 

response to longwall mining. In Proceeding of the 7th Int. 

Conf. Ground Control in Mining, 149-162. 

7. Brutcher, D.F., B.B. Mehnert, D.J. van Roosendal and 

R.A. Bauer. 1990. Rock strength and overburden 

changes due to subsidence over a longwall coal mining 

operation in Illinois. Rock Mechanics Contributions and 

Challenges, 31st US Symposium on Rock Mechanics, 

Golden Colorado, 563-570. 

8. Booth, C.J. and E.D. Spande. 1992. Potentiometric and 

aquifer property changes above subsiding longwall mine 

panels, Illinois basin coalfield. Ground Water 30: 2, 362-

368. 

9. Matetic, R.J., J. Liu and D. Elsworth. 1995. Using 

Multiple-point borehole extensometers and finite element 

modeling in coordination with hydrologic field data to 

determine post-mining effects to the ground water system. 

Outdoor Action Conference, National Ground Water 

Association, Las Vegas, NV, 2 - 9 May 1995. 

10. Wang, J.A. and H.D. Park. 2002. Fluid permeability of 

sedimentary rocks in a complete stress-strain process. 

Engineering Geology 63: 291-300. 

11. Bai, M. and D. Elsworth. 1993. Influence of mining 

geometry on mine hydro-geo-mechanics. SME Annual 

Meeting, Reno, Nevada, Preprint 93-6. 

12. Hoek, E. and J.W. Bray. 1981. Rock slope engineering. 

3rd edition, Inst. Min. Metall. London. 

13. Louis, C. 1969. A study of groundwater flow in jointed 

rock and its influence on the stability of rock masses. 

PhD thesis, Univ. Karlsruhe. 

14. Lowndes, I.S., D.J. Reddish, T.X. Ren, D.N. Whittles, 

and D.M. Hargreaves. 2002. Improved modeling to 

support the prediction of gas migration and emission from 

active longwall panels. Mine Ventilation. eds. Euler De 

Souza, Balkema, 267-272. 

15. Peng, S.S.. 1984. Longwall Mining, Wiley, New York. 

16. Singh, M.M. and F.S. Kendorski. 1981. Strata 

disturbance prediction for mining beneath surface water 

and waste impoundments. 1st Conf. Ground Control in 

Mining, Morgantown, WV, 76-89. 

17. Pappas, D.M. and C. Mark.. 1993. Behavior of simulated 

gob material. U.S. Bureau of Mines Report of 

Investigations RI 9458.

18. Whittaker, B.N., R.N. Singh and C.J. Neate. 1979. Effect 

of longwall mining on ground permeability and 

subsurface drainage. In Proceedings of the First 

International Mine Drainage Symposium, 161-183. 

19. Diamond, W.P., J.P. Ulery, and S. J. Kravitz,. 1992. 

Determining the source of longwall gob gas: Lower 

Kittanning Coalbed, Cambria County, PA. Bureau of 

Mines, Information Circular No: 9430. 

20. Diamond, W.P. 1994. Methane Control for Underground 

Coal Mines. U.S. Bureau of Mines, Information Circular 

9395.

21. Mucho, T. 2004. Personal communication. 

22. Rusnak, J.A. and C. Mark. 1999. Using the point load 

test to determine the uniaxial compressive strength of coal 

measure rock. In Proceeding of the 19th Int. Conference 

on Ground Control in Mining, 362-371. 

23. Mark, C. and T.P. Mucho. 1994. Longwall mine design 

for the control of high horizontal stress. In Proceeding of 

the New Technology for Longwall Ground Control, US 

Bureau of Mines, Special Publication 01-94, 53-76. 

24. Dolinar, D.R. 2003. Variation of horizontal stresses and 

strains in mines in bedded deposits in the eastern and 

midwestern United States. In Proceeding of the 22nd Int 

Conf. Ground Control in Mining, Morgantown, West 

Virginia, 178-185. 

25. Franklin J.A. and M.B. Dusseault. 1989. Rock 

Engineering, McGraw-Hill. 

26. Leedan, Van der F., F.L. Troise and D.K. Todd. 1991. 

The Water Encyclopedia. 2nd edition, Lewis, Michigan. 

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

techniques to maximize methane capture from surface 

gob wells. Mine Ventilation: 279-286. 

28. Ertekin, T., W. Sung, and F.C. Schwerer. 1988. 

Production performance analysis of horizontal drainage 

wells for the degasification of coal seams. Journal of 

Petroleum Technology, May, 625-631. 

29. King, G. and Ertekin, T. 1991. State of the Art Modeling 

for Unconventional Gas Recovery, SPE Formation 

Evaluation, March, pp. 63-72. 

30. Young, G.B.C., G.W. Paul, J.L. Saulsberry, and R.A. 

Schraufnagel. 1993. A simulation-based analysis of 

multiseam coalbed well completions, SPE Paper 26628, 

In Proceedings of the 68th Society of Petroleum 

Engineers Annual Technical Conference and Exhibition,

205-215. 

31. Karacan, C.O., G.S. Esterhuizen, S. Schatzel, and W.P. 

Diamond. 2005. Reservoir simulation-based modeling 

for characterizing longwall methane emissions and gob 

gas venthole production. Int J. Coal Geology (to be 

published). 

32. Garcia F. Personal communication, 2003.