Forecasting gob gas venthole production performances using intelligent computing

methods for optimum methane control in longwall coal mines

C. Özgen Karacan ⁎

National Institute for Occupational Safety and Health (NIOSH), Pittsburgh Research Laboratory, Pittsburgh, PA 15236, USA

abstract

Gob gas ventholes (GGV) are used to control methane inflows into a longwall operation by capturing it

within the overlying fractured strata before it enters the work environment. Thus, it is important to

understand the effects of various factors, such as drilling parameters, location of borehole, applied vacuum by

exhausters and mining/panel parameters in order to be able to evaluate the performance of GGVs and to

predict their effectiveness in controlling methane emissions. However, a practical model for this purpose

currently does not exist.

In this paper, we analyzed the total gas flow rates and methane percentages from 10 GGVs located on three

adjacent panels operated in Pittsburgh coalbed in Southwestern Pennsylvania section of Northern

Appalachian basin. The ventholes were drilled from different surface elevations and were located at varying

distances from the start-up ends of the panels and from the tailgate entries. Exhauster pressures, casing

diameters, location of longwall face and mining rates and production data were also recorded. These data

were incorporated into a multilayer-perceptron (MLP) type artificial neural network (ANN) to model

venthole production. The results showed that the two-hidden layer model predicted total production and the

methane content of the GGVs with more than 90% accuracy. The ANN model was further used to conduct

sensitivity analyses about the mean of the input variables to determine the effect of each input variable on

the predicted production performance of GGVs.

1. Introduction

Longwall mining is a continuous process in an extensive area, where

the roof is supported only temporarily during mining with hydraulic

supports that protect the workers and the equipment on the face. As the

coal is extracted, the supports automatically advance with the rate of

mining, and the roof strata are allowed to cave behind the supports.

The caved zone created by longwall mining is highly fragmented, and

generally extends upwards three to six times the thickness of the mined

coalbed.

Above the caved zone is a fractured zone characterized by mininginduced vertical fractures and horizontal fractures caused by separations along bedding planes. The thickness of the fractured zone can

vary up to 100 times the height of the mined coalbed depending on

the characteristics of the associated rock strata, thickness of the

overburden, and the size of the longwall panel (Palchik, 2003). Any

gas that is contained within the coal beds in this area of relieved stress,

which is called “gob”, will be released slowly over time, while free gas

in other porous formations, such as sandstones, will be released more

quickly. Relaxation of the roof rocks and the associated fracture connectivity allows gas to flow from all surrounding gas sources toward

the underground workings, which eventually may create an unsafe

condition for the underground workforce.

One common technique to control methane emissions is to drill

vertical gob gas ventholes into each longwall panel to capture the

methane within the overlying fractured strata before it enters the

work environment. Although the drilling practices of GGVs may

change based on local conditions, most gob gas ventholes are drilled

to within a short distance, 10–30 m, of the coal bed being mined and

cased with steel pipe. Commonly, the bottom section of the casing

(generally about 60 m) is slotted and placed adjacent to the expected

gas production zone in the overburden strata. The usual practice is to

drill the gob gas ventholes prior to mining. As mining advances under

the venthole, the gas-bearing strata that surround the well will

fracture and establish preferential pathways for the released gas to

flow towards the ventholes (Diamond et al., 1994).

Exhausters are placed on gob gas ventholes to maintain a vacuum

on the wellbore to induce gas flow towards the venthole. Gas production may exhibit variable gas quality. In the early stages of

production, the gas quality is generally high (N80% methane) after a

hole is intercepted by the longwall. Relatively high production rates

with high methane quality are usually sustained for a few weeks. Later

in time, especially towards the end of the panel or after the panel is

completed, gob gas production may exhibit decreased methane levels

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as ventilation air is drawn from the active mine workings. Commonly,

when the methane concentration in the produced gas reaches 25%, the

exhausters are shut down as a safety measure, and the holes may be

allowed to free flow.

It is difficult to predict production performance of gob gas

ventholes due to the involvement of multiple influential factors.

Currently, a detailed model does not exist that realistically represents

the multiple variables associated with underground coal mining

operations and their interactions and influences on the performance

of gob gas ventholes. Previously, Lunarzewski (1998) used boundary

element and sequential bed separation methods for floor and roof

strata relaxation and immediate roof bending separation, as well as

gas emission rate calculations, to develop “Floorgas” and “Roofgas”

simulation programs to characterize the strata relaxation zones, gas

emission boundaries, and parameters for gas emission prediction.

Palchik (2002) proposed a new mathematical model for estimation of

productivity of gob gas ventholes associated with coal mining in the

Torezko–Snezhnyanskya coal district in Donetsk, Ukraine. He established, for the specific conditions he studied, a relationship between

methane emission from GGVs and the duration of well production

using the Gaussian error distribution function and the Gaussian

density function. These models allowed prediction of total volume of

gas, duration of well production, and the time when maximum

production rate occurred. However, he noted that in the mining area

he studied that 87–95% of total gob gas originated from the mined coal

itself and the contribution from additional gas resources over the

extracted seam was insignificant. Also, the ventholes were free

flowing without any vacuum pressure. Thus, his analytical model is

naturally simplified and may not represent most situations, at least for

the mines in the U.S. where the gob gas generally originates from the

fractured strata overlying the extracted seam and flow is supported

and maintained by an applied vacuum.

Ren and Edwards (2002) used a computational fluid dynamics

(CFD) modeling approach to investigate gas capture from surface gob

gas ventholes. That paper introduced how this approach could be used

to improve the design of surface gob wells for methane recovery while

minimizing the leakage of air into the gob. Tomita et al. (2003)

developed a three-dimensional (3-D) finite element model (FEM) to

predict the volume of methane gas emitted from surrounding coal and

rock layers based on stress distribution and permeability changes.

Karacan et al. (2005) developed a 3-D model of a longwall mining

district to simulate the effects of longwall panel width on methane

emissions and on the performance of gob gas ventholes. The focus of

that effort was the prediction of the incremental change in methane

emissions to be expected due to increasing panel width and the

optimization of gob gas venthole completion and placement practices

to capture more of this gas, thus preventing it from entering the

underground workplace. Karacan et al. (2007) used reservoir

simulation to investigate the effects of various completion parameters

on gob gas venthole performance. In Karacan et al. (2005, 2007),

moving boundary conditions were also added to the simulations,

which made them more realistic but extremely difficult to model and

simulate.

Despite the improvements in analytical and numerical modeling

approaches, experience suggests that it is still difficult to accurately

predict methane production for gob gas ventholes and that these

predictions may be underestimated relative to actual gob gas venthole

production by at least a factor of two or more. The key factor to this

underestimation is the difficulty in incorporating in any predictive

approach the many factors that affect venthole performance (Zuber,

1998). And in almost all cases, these factors are related non-linearly to

each other and to the production performances. Thus, production

predictions may be more successful if these non-linearities can be

understood and unconventional modeling techniques, such as artificial

neural networks (ANN) that can handle these non-linearities, can be

used. ANNs are adaptable systems that can determine relationships

between different sets of data to solve problems where conventional

computer models are inefficient. These problems are either the nonpolynomial type or the very complex problems that are difficult to

describe mathematically. The key advantage of neural networks is their

ability to recognize patterns between input and output space and to

generalize solutions. However, a high set of input and output data is

required to calibrate the ANN, which some cases limits the applicability

of this technique.

ANNs have found their way into many fields of application due to

their flexibility and accuracy when compared to statistical models.

Patel et al. (2007) used artificial neural networks for estimation of

gross calorific values of coals. Bagherieh et al. (2007) used the

technique to study the relationships between petrography and

grindability of Kentucky coals. Karacan (2007) integrated reservoir

simulations during development mining with ANN to predict

methane emissions into roadways and to optimize ventilation air

requirements. Karacan (2008a, 2009) used artificial neural networks

to model ventilation methane emissions from longwall mines and to

select a proper degasification system using various factors. Maier and

Dandy (2000) provided an excellent overview of modeling issues and

applications of ANN for forecasting in engineering problems. The

flexibility of ANNs and their accuracy make them a good candidate for

use in evaluating and predicting gob gas venthole performances.

1.1. Objective of the study

The aim of this paper is to develop an ANN-based methodology to

predict gob gas venthole production rates and methane concentrations based on venthole location, mining parameters, borehole

location with respect to panel and surface terrain, and exhauster

pressure. The data were obtained from 10 gob gas ventholes drilled

over three adjacent panels mined in the Pittsburgh coal bed in the

Southwestern Pennsylvania section of the Northern Appalachian

basin. The production data were obtained by monitoring these

ventholes for flow rate and methane concentration during and after

panel extraction. The ANN model was built using a multilayer

perceptron (MLP) approach and was trained (calibrated) and tested

using the database to achieve minimum mean square error and high

correlations between measurements and predictions. After a reasonable prediction with ANN was obtained, sensitivity analyses were run

about the means of input variables to better understand the effects of

each of these inputs on predicted outputs.

2. Drilling locations and completion parameters of the gob

gas ventholes

The Northern Appalachian Basin in Southwestern Pennsylvania is a

very important area for coal mining and for mining-related methane

emission and capture using conventional boreholes and gob gas

ventholes. Gob gas ventholes are commonly used to control the

methane emissions from the fractured zone and are drilled from the

surface to a depth that places them above the caved zone. The bottom

section of the pipe is slotted and placed adjacent to the expected gas

production zone.

The production performance of gob gas ventholes can be closely

related to their locations, panel parameters, borehole completions,

wellhead designs, and operation of the exhausters. Fig. 1 shows the

locations of the ventholes that are evaluated in this study. Markowski

(1998) reported that the main coal beds that are consistent and

continuous in the study area of this paper are the Pittsburgh,

Sewickley, and Waynesburg. The Redstone coal bed and Pittsburgh

rider coals shown in Fig. 2-A are not continuous and can be present at

some locations. In this geology, gob gas ventholes are generally drilled

to within 12 m above top of the main coal seam in that mining area

and are completed with a 61 m slotted casing at the bottom as shown

in Fig. 2-B. Due to different drilling practices, however, the proximity
of slotted casing's bottom to the top of the coal bed may change from

venthole to venthole, although the length of the slotted section is kept

more or less constant.

The location of a GGV with respect to the panel tailgate and panel

start is usually calculated based on the subsidence profiles expected

during mining in order to locate the ventholes in tension zones, where

most fractures are open, to ensure maximum production from the

fractured strata. The distances from the start of the panels and the

distances between the ventholes along the panel are again based on

maximizing productivity, on expected drainage radius of the ventholes, and on the emissions in the mines. It has been shown that the

location of the ventholes on the panel is important for their

performance (Diamond et al., 1994). In general, the holes on the

ends of the panels are the highest-quantity and longest-duration

producers. This is attributed to enhanced mining-induced fractures on

the ends of the panels where the overburden strata are in tension on

three sides due to the support of the surrounding pillars. Thus,

placement of gob gas ventholes in the zone of tension along the

margins of a panel, instead of in the traditional centerline location

which is in compression due to subsidence and re-compaction of the

longwall gob, may lead better production characteristics. Analysis of

seven months of gas production from the Lower Kittanning Seam

showed that the near-margin holes produced 77% more gas than did

centerline holes on the same panel (Diamond et al., 1994).

Although the importance of placement of gob gas ventholes over

the longwall panel for optimum productivity are recognized by many

mining companies, the desired locations initially planned for drilling

may change due to land ownership issues and due to the accessibility

of planned drilling locations. These changes may lead to the ventholes

being drilled at different locations (hill tops or valleys). Based on an

earlier integrated study evaluating hydraulic properties of underground strata and their potential responses to longwall mining,

ventholes drilled in the valleys may be more productive than the ones

drilled on hill tops (Karacan and Goodman, in press) due to preexistence of structural fractures or fractures that can be extended

easily (Wyrick and Borchers, 1981). Karacan and Goodman (in press)

observed that the borehole location affects the fracturing time where

hill-top boreholes seem to fracture earlier than valley-bottom wells.

However, the permeability of the factures at the hill-top wells is less

compared to that of valley-bottom wells due to greater overburden

depths. Greater overburden depths usually cause lower hydraulic

conductivities and potentially less effective boreholes as opposed to

shallow GGVs. As the well locations approached the start up entry,

average hydraulic conductivities were potentially higher indicating

better production performances.

Casing diameter is another factor to be considered in calculating

gob gas venthole performances. An earlier study (Karacan et al., 2007)

showed that keeping the other completion parameters constant,

increasing the gob gas venthole 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, the

increased gas flow rate increased the overall volume of methane

produced. Also, casing setting depth with respect to the mined coal

seam may play an important role on the amount and concentration of

methane captured. Reservoir modeling results (Karacan et al., 2007)

showed that when the setting depth was close to or within the caved

zone, the methane concentration and the total amount of methane

captured decreased. However, one additional consideration for

changing the 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.

Borehole location and completion parameters impact gob gas

venthole production performance in many ways and, for this reason,

they should not be excluded from performance analyses. Table 1 gives

the actual drilling and completion details of the 10 ventholes

monitored for their production in this study. As this table shows,

panel dimensions and venthole completion details differ. Thus, varying

production performances can be expected from these ventholes.

3. Longwall face advance rates of studied panels

Gob gas venthole production performance during longwall mining

is closely related to face advance rate that enhances fractures and

increases permeability due to dynamic deformation and subsidence

(Preusse, 2001). Karacan and Goodman (in press) observed that

Fig. 1. Map of adjacent panels that were monitored for mining activities. Longwall mining started at Panel A and moved to Panel C as each panel was completed. The drill locations of

10 GGVs that were monitored for their production performances for a period of almost two years are also shown in this figure. Triple-horizontal lines are the entries for each panel.
Table 1

Location, drilling and completion parameters of the monitored gob gas ventholes shown in Fig. 1.

Venthole Surface elevation

(m)

Overburden

(m)

Distance to coal seam

(m)

Casing diameter

(cm)

Distance to tailgate

(m)

Distance from panel start

(m)

Panel width

(m)

Panel length

(m)

A-1 413.55 252.62 10.67 17.78 76.20 167.64 373.38 3361.94

A-2 379.21 215.23 10.67 17.78 79.25 822.96 373.38 3361.94

A-3 387.65 219.71 10.67 17.78 83.82 1623.06 373.38 3361.94

A-4 384.35 208.79 12.19 17.78 92.66 2404.87 373.38 3361.94

B-1 355.45 196.34 13.72 20.32 99.97 113.39 373.38 3291.23

B-2 402.87 240.72 13.72 20.32 73.15 649.83 373.38 3291.23

B-3 421.79 257.50 13.72 17.78 89.92 1369.16 373.38 3291.23

B-4 394.41 222.81 12.19 17.78 78.03 2094.59 373.38 3291.23

C-1 348.39 192.02 13.72 17.78 92.96 146.91 434.34 3379.01

C-2 444.70 282.85 14.63 17.78 78.03 934.52 434.34 3379.01

Fig. 2. A schematic representation of general stratigraphy over Pittsburgh coal bed in the Southwestern Pennsylvania section of Northern Appalachian basin and the typical gob gas

venthole configuration that is used for methane control in longwall mines in this region (A). The depths and lengths shown in this figure are only representative numbers and can

change based on the site-specific conditions. Panel B shows a picture of a typical slotted casing ready on the drill rig to be set into the venthole.
higher face advance rates resulted in lower hydraulic conductivity in

the fractures, suggesting a possible impact on GGV performance.

Face advance also creates an extensively fractured methane

reservoir from which the gob gas ventholes can produce gas. A faster

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. Down times due to mechanical delays or vacation

periods halt the expansion of fractured methane reservoir which

eventually may affect gas production rates and methane percentages

from gob gas ventholes.

The face advance rates reported by the mining company during

longwall operation of A–C panels were gathered. The daily advance

rates and the cumulative panel lengths mined were plotted. These

data are shown in Fig. 3.

Fig. 3 shows the mining durations for each panel from the

beginning of longwall operation. It took between 200 and 275 days

to mine each of these panels and a total of 730 days to mine them all.

In this graph, both daily advance rates and the cumulative panel

percentage mined are shown as functions of mining duration. As it is

presented in this figure, daily advance rates change from 0 m/day

(face stoppage) to 25–30 m/day for all panels. Face stoppages are

usually associated with mechanical delays at the face and they do not

take too long for the mining to resume. Longest face stoppages

occurred during miner's vacation periods as panels A and C were being

mined. These periods were usually 14 days. The average face advance

rates for these panels were between 10 to 15 m/day and it took

approximately 4 months to mine 50% of each panel.

4. Measured wellhead vacuum pressures and gob gas venthole

production histories

4.1. Evaluation of atmospheric pressures and the average vacuum values

measured at the wellheads

In order to monitor production history of the gob gas ventholes in

three adjacent longwall panels, the wellheads were instrumented

with flow meters, pressure sensors, temperature sensors and methane

detectors that continuously recorded the total gas flow, methane

percentage, prevailing wellhead vacuum, temperature and atmospheric pressure data. Vacuum at the wellhead was measured at two

points; above and below the flame arrestor to diagnose any plugging

or pump operation problems. The average vacuum value reported in

this study is the arithmetic average of these two measurements.

Fig. 4 shows the measured atmospheric pressures (top figure) and

the average vacuum level prevailing at the wellheads (bottom figure)

of each venthole due to operation of methane exhausters. This figure

shows that atmospheric pressure fluctuates between 96 and 98 KPa at

the monitoring sites. A general trendline in the figure shows the

magnitude and characteristics of these fluctuations. These variations

in recorded atmospheric pressure are seasonal and also due to the

elevation of the boreholes (Table 1). The bottom figure, on the other

hand, shows the variations in vacuum pressure and exhibits a more

erratic behavior depending on the operation of the methane-driven

engines. The exhausters used in monitored ventholes are four stage

centrifugal pumps that are normally powered by diesel engines.

However, these were converted to operate on methane mixtures for

these applications. Thus, the vacuums generated by these systems are

quite variable and may depend on fuel quality and air quality.

It is known that 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. The

impact of barometric pressure fluctuations on the composition and

emission of the landfill gas has been investigated (Nastev et al., 2001).

They observed that when the barometric pressure increased, gas flux at

the landfill surface decreased and even became negative probably

because of the counter pressure. This resulted in atmospheric air

intrusions in the upper landfill layers. On the other hand, landfill gas

emissions increased with decreasing atmospheric pressures. In both

cases, all of the gases regained their initial equilibrium fluxes, with a

corresponding time delay in the gas composition response. Thus, they

concluded that the rate of barometric pressure variation is an

important factor to consider when sampling landfill gas. In a different

study, the US Environmental Protection Agency (2004) collected gas

flow and quality data during the spring of 1999 from a mine that was

abandoned but had been venting to atmosphere since 1962. They

correlated flow and methane percentage data to changes in atmospheric pressure. That study indicated that there was a strong inverse

relationship between barometric pressure and methane emissions

from the mine vent. However, this situation can be definitely different

for closed sources underground as they will not have direct contact

with the atmosphere. Although Mucho et al. (2000) suggested that

pump elevation affects its operation because the atmospheric pressure

Fig. 3. Daily linear face advance rates and cumulative percentage of the lengths that have been mined for each panel.
and oxygen content change, Fig. 4 does not show a clear relation

between atmospheric pressure and the vacuum generated by the

pumps because it depends more on the fuel quality.

A vacuum applied to the gob gas wellheads stimulates methane

migration into the ventholes from the surrounding strata and prevents

occasional flow reversals (Thakur, 2006). However, this advantage

may be lost over time as there is tendency for mine air to be drawn

into the gob area and dilute the methane (Ren and Edwards, 2002). A

higher suction pressure has a positive but relatively small effect on

drawing gas from overlying strata into the venthole. There is a limit to

the amount by which the capture efficiency of a venthole can be

increased by means of higher vacuum pressures. Higher suction

pressure causes higher air leakage into the well and reduces the purity

of methane (Mazza and Mlinar, 1977; Zuber, 1998; Mucho et al., 2000).

In the monitored ventholes, most vacuums were less than 15 KPa

(Fig. 4). For instance, during mining of Panel A, the vacuum on A-1

venthole was consistently high (13 KPa) for about 200 days, then

decreased to 6–7 KPa during mining of Panel B and stayed at that level

until the end of the production from this borehole. On the other hand,

A-2 and A-3 ventholes started with pressures less than 5 KPa and,

except for short-lived high vacuum values, stayed low (Fig. 4). The

average vacuum pressure trends observed in the ventholes of Panel B

(B-1 to 4) during mining and after completion of this panel are more

expected. They started with low vacuum values, increased as mining

continued, and later decreased possibly due to low flow rates.

4.2. Evaluation of total gas production rates—performance parameter 1

A production well's performance can be measured by various

parameters, such as oil production (for oil wells), condensate

production (for condensate wells), water production, water cut, gas

composition, etc. Since the gob gas ventholes do not usually produce

water and the main production is the gob gas composed of two main

components (air and methane), total gas production and methane

percentage, from which pure methane production can be calculated,

were selected as performance parameters in this study.

Fig. 5 shows the total gas production history of monitored gob gas

ventholes. Although the data is scattered, the general trends in

observed gas productions show that the ventholes initially produce at

higher gas rates and then enter a decline period with increasing time.

Fig. 4. Atmospheric pressure recordings and average vacuum values applied by the exhauster pump at the wellhead of each venthole. Average vacuum value was calculated based on

the measured vacuum values below and above the flame arrestor at the wellhead. Vertical dashed lines indicate the start and completion days of the panels and the durations of

mining in each panel. The short intervals between consecutive dashed lines are the durations where longwall moves occurred from one panel location to the next.
Most of the ventholes continue producing even after the panel, over

which they are located, is completed. This figure shows that, even after

panel completion, there is still enough gas emission in the gob for the

ventholes to continue production. However, comparison of mining and

after-mining periods of production generally shows that the production rate decline is faster after the corresponding panel is completed.

Another observation from the data in Fig. 5 corroborates earlier

reports from a different site (Mucho et al., 2000); the first boreholes in

each panel are the longest producers due to their close proximity to

the panel start up entries. They are also expected to be the producers

with the highest gas rate (Mucho et al., 2000) but the production

history of A-1 shows that this is not always the case. However, they

typically produce the highest methane percentage (Fig. 6). This is

more obvious in panel A but not in panel B. This may be due to the

adjacent nature of these two panels.

The position of the ventholes may affect their production potential.

Ventholes drilled into areas where fractures extend towards the

methane source are likely to achieve higher capture efficiencies while

ventholes drilled into low-permeability strata or compacted portions of

the gob will likely have a lower production rate even with a higher

suction pressure. Comparison of Fig. 5 with Fig. 4 shows that total gas

production rate is generally positively related to applied vacuum. In

other words, as a harder vacuum is applied, the production rate from the

ventholes somewhat increases. Adapting oil-field terms in this study,

this indicates a relatively constant productivity index (PI), which is

defined as the ability of a reservoir to deliver fluids to the wellbore and is

stated as the volume delivered per pressure drawdown. Based on the

data, the PI was calculated to be within 500–3000 m3

/day/KPa for this

gob reservoir. This also shows that the productivity of gob gas ventholes

can only be increased within a certain range with applied vacuum.

Fig. 5. Total gas production rate from each of the monitored ventholes in the panels. Vertical dashed lines indicate the start and completion days of the panels and the durations of

mining in each panel. The short intervals between consecutive dashed lines are the durations where longwall moves occurred from one panel location to the next.

Fig. 6. Methane concentrations measured in the total production gas stream of each of the ventholes. Multiplication of this concentration with the total production gives methane

production from each borehole. Vertical dashed lines indicate the start and completion days of the panels and the durations of mining in each panel. The short intervals between

consecutive dashed lines are the durations where longwall moves occurred from one panel location to the next.
Tracer gas studies reported in Mucho et al. (2000) showed that gob

gas ventholes in their study area were in communication with each

other through the fractures within the overlying strata. They observed

that SF6 gas injected into an intaking venthole was detected at the

next ventholes within hours to days depending on the distance. A

similar observation for pressure communication was reported by

Schatzel et al. (2008) who reported a decline in shut-in pressure of a

monitoring borehole when a gob gas venthole became operational

76 m away from its location. Although, the ventholes monitored in this

study were probably also in communication with each other in some

ways, it was difficult to observe these effects from the production

histories given in Fig. 5. However, this is another complicating factor in

evaluation of gob gas venthole performances.

4.3. Evaluation of methane concentrations—performance parameter 2

The second performance parameter evaluated in this study is the

methane concentration measured in venthole productions. Fig. 6

shows that methane concentration changes during the production life

of the ventholes. As expected, ventholes generally start with higher

methane concentration which then declines with time. That decline

may be associated with the changes in wellhead vacuum and also with

the production of available methane in the gob. Earlier researchers

(Mazza and Mlinar, 1977; Mucho et al., 2000; Ren and Edwards, 2002)

suggested that as vacuum is increased, especially close to or after

completion of the panel, the venthole may start producing mine air.

This suggestion is supported with the data in this study too. As a clear

example, the decline in methane concentration from 80% to 40% in A-4

production during mining of Panel B (Fig. 6) is associated with an

increase in average wellhead vacuum from −4 to −7 KPa (Fig. 4).

There are also two sets of methane concentration behaviors in Fig. 6

that are notable and worth discussing. The first one is the behavior of A-1

during and after completion of the panel. Although the average wellhead

vacuum pressure dropped from ~−15 KPa to ~−5 KPa (Fig. 4), the

methane concentration did not change that much. This may be due to

the fact that this venthole was the first one in the first panel of a series of

new panels to be developed in a new district. Thus, it was surrounded

with virgin coal beds and other sources of methane, which might have

helped it to produce high quality methane for a long time.

The second observation is the behavior of Panel B ventholes,

particularly B-1, B-2 and B-3. During mining of Panel B, methane

concentration from these ventholes declined steadily until the

completion of Panel B and the start of mining in Panel C (Fig. 6).

After Panel C mining starts, methane concentrations from these

ventholes increase again towards their original concentrations with

associated decreases in wellhead vacuum values, although total gas

flow rates continue declining (Fig. 5). This behavior may be due to

formation of the gob in Panel C, which extends the size of the methane

reservoir available to Panel B ventholes.

5. Development and application of artificial neural networks to

predict gob gas venthole performances

The previous sections discussed those factors that affect gob gas

venthole performance. The difficulties encountered and the lack of

knowledge about the relationships between them when incorporating

these factors into a model makes ANNs one of the better candidates to

predict gob gas venthole performances. ANNs can recognize the

patterns between input and output space where non-polynomial type

or mathematically very complex relations are present and can

generalize solutions to forecast gob gas venthole performance.

Basically, a neural network is a computing tool that processes

information by its dynamic state response to external inputs. One of

the most widely used neural network topologies is the multilayer

perceptron (MLP) because of its applicability to different problems

(Hagan et al., 1997; Schalkoff, 1997) using a non-linear activation

function (such as hyperbolic tangent) between its layers of processing

elements. The non-linear nature of the activation function plays an

important role in the performance of a neural network.

The neural network computes its output at each iteration (epoch)

and compares it with the expected output of each input (exemplar)

vector in order to calculate the error. The most widely used technique is

to propagate the error back (back-propagation) and adjust the initially

assigned random weights for each processing element. Minimizing the

mean square error (MSE) is the goal of the optimization (training)

process. During error minimization, it is preferable to find the global

optimum rather than local optima. This situation is helped by applying

a momentum factor between 0 and 1. Momentum increases the

effective step size in shallow regions of error surface (Hassoun, 1995)

and helps speed up the training process. This is the training technique

used in the modeling described in this paper. Cross validation and

testing are the performance measures of the network with new data

that give confidence that the network is able to recognize the training

patterns and generalize the relationships within a certain degree of

acceptable error.

Since it has been established in other studies before (Eberhart and

Dobbins, 1990; Mohaghegh, 2000; Maier and Dandy, 2000; Karacan,

2007, 2008a, 2009) the basic principles and components of an ANN

model will not be repeated here. Readers are referred to these

references for detailed discussions of ANNs and their uses in prediction and classification problems.

5.1. Database preparation and pre-processing for GGV performance

prediction

In order to develop a database for modeling gob gas venthole

productions and methane percentages, the data given in Table 1 and in

Figs. 3–6 were combined and analyzed. These sources provided panel

dimensions, venthole location details, venthole completion details,

daily mining rate and daily completion percentage of the panels, daily

average wellhead vacuum, daily barometric pressure readings, daily

average production rate, and daily average methane percentage

values. In addition to these, the conditions such as whether the

panel was completed (0 for “Yes”) or not (1 for “No”) – to differentiate

between borehole productions during mining and after completion of

the panels, and whether there is face advancing (1 for “Yes”) or not (0

for “No”), and to differentiate the coal production from idle periods,

were introduced as additional parameters. With merging of all these

daily data resulted in a database consisting of 16 parameters each

having 1972 entries. Of these 16 parameters, 2 of them (gas production and methane percentage) are outputs to be determined using

the remaining 14 variables (Fig. 7).

For pre-processing of these 16 × 1972 data, the whole dataset

(exemplars) was first randomized and then separated into three

sections for use in the training, cross validation, and testing phases.

Randomization was used to prevent the biases and to make different

sections of the dataset representative of the whole population. In

separating the dataset, 197 out of 1972 exemplars (10%) were saved as

cross validation data, 296 exemplars (15%) were saved as testing and

1672 exemplars (75%) were saved for training data. The criterion in

breaking up the data was to allocate enough exemplars for the ANN

training and testing.

5.2. Development of ANN models

The development and implementation of the ANN model were

performed using NeuroSolutionsTM v5.70 software (NeuroDimension,

2008). In order to determine an appropriate ANN topology and to

identify network parameters for training and testing, various combinations of parameters were tested. Values of minimum and final mean

squared error (MSE) were noted for training and cross validation of the

ANN. Also, values of nominal mean squared error (NMSE), regression
coefficient, and absolute error were noted for testing the ANN. The

number of hidden layers, the number of processing element in hidden

layers, the number of training iterations, the type of transfer function,

and the value of momentum were varied. The details of this procedure

were given in Karacan (2007, 2008a, 2009). Based on this iterative and

interactive procedure, a two-layer model with 22 processing elements

in the first hidden layer and with 16 processing elements in the second

hidden layer was selected as the basic architecture of the networks.

Minimal training errors were noted when using the hyperbolic tangent

activation function, “Tanh,” between the layers with momentum

parameters of 0.7 and 2500 iterations conducted twice. The schematic

illustration of the architecture and model parameters, as well as inputs

and outputs are shown in Fig. 7.

Table 2 shows the performance parameters for learning with cross

validation for the network shown in Fig. 7. These data shows that

minimum and final mean squared errors in training for the average of

2 runs are very low, 0.00416 and 0.00426, respectively, and they are

close to each other. This shows that the iteration that gives the least

error happens before the completion of 2500th (final) iteration.

Average mean squared errors in cross validation are low too, 0.00446

and 0.00454 for the iteration that had the minimum MSE and final

iterations, respectively. The fact that cross validation errors are slightly

higher than, but close to, training errors indicates that the network

was optimized and that the network did not memorize the pattern of

inputs. In addition, both training and cross validation standard

deviations were low, indicating that there was a consistency in the

average MSE obtained during runs. These are favorable indications of a

good training (Eberhart and Dobbins, 1990; Maier and Dandy, 2000).

Table 2 also shows the best network run and iteration where the

minimum MSE for training and cross validation occurred. According to

these results the minimum MSE for training and cross validation

occurred as 0.00412 and 0.00429 at the 2484th and 2492nd iteration

of the 1st training run. Thus, the calibration “weights” obtained during

these iterations were saved by the network to be used in the testing

and prediction stages.

5.3. Testing and prediction capability of developed ANN model for gob

gas performance prediction

The trained network was tested in two stages. In the first stage, the

network was tested using the cross validation data. The same data set

that was used in cross validation in training phase was presented to

the network in the testing phase to note the prediction performance.

In the second stage of testing, the ANN was exposed to an entirely new

data set (testing set) that it had not yet seen. This is an important test

to see if the ANN generates acceptable predictions for the new data

patterns or, in other words, to see whether it recognizes the patterns

and generalizes the results with which it was trained. In some cases, a

trained network may give close predictions to the cross validation set,

since it has seen it before. However, poor predictive performance

towards the testing data set suggests that the ANN cannot generalize

Fig. 7. ANN topology and its parameters that were developed to predict gob gas venthole performances.

Table 2

Average (minimum and final) mean squared training errors (MSE) and their standard

deviations obtained from the ANN (Fig. 7) using 2 × 2500 training with cross validation.

Training

minimum

Training

standard

deviation

Cross

validation

minimum

Cross validation

standard deviation

Average of

minimum MSEs

0.00416 0.000049 0.00446 0.000237

Average of final

MSEs

0.00426 0.000102 0.00454 0.000124

Best iteration Training Cross validation

Run 1 1

Iteration no 2484 2492

Minimum MSE 0.00412 0.00429

Final MSE 0.00434 0.00445

The table also shows the iterations where the best results obtained.
or recognize patterns and this may indicate memorization. Therefore,

two-stage testing should give an idea about the performance and thus

predictive capability of the trained ANNs.

Table 3 shows the prediction performance of the network using

cross validation data set. This table gives the nominal MSE (MSE/

variance of the desired outputs), mean, minimum and maximum

absolute errors as well as the correlation coefficient, R, when compared

with the total flow and methane percentage data of the cross validation

set. It is seen that nominal MSE is low for both flow and methane

percentage. Absolute errors can be considered low too except for some

outliers in flow and percent methane that gave relatively higher

maximum absolute errors. Nevertheless, the correlation coefficients

(R) are very high, indicating a generally good prediction performance.

In the second stage of testing, the “testing” set which was saved in

the pre-processing stage for testing and which was composed of 296

exemplars (data) was given to the network to predict the flow and

methane percent values. The performance indicator results are given in

Table 4. This table shows that the prediction performance of the data

set is generally good again, except for the higher maximum absolute

error in the flow data. These extreme points, originating most probably

from the outliers in the flow data in any one of the ventholes, are

difficult for the network to capture. However, correlation coefficients

for both total flow and methane percent are quite high despite the

erratic variations in the data shown in Figs. 4 and 5.

Figs. 8 and 9 show the plots of measured total gas flow rates versus

predicted value, and measured methane percentages versus the

predicted values, respectively, using the ANN model for the testing

set. Fig. 8 shows that the ANN model is capable of predicting the total

gas flow data reasonably well (R= 0.93; R2= 0.85). Similarly, Fig. 9

shows that methane percentages also can be predicted well using the

network. Methane percentages can be predicted with an R of 0.95 and

an R2 of 0.91. In general, the comparison of the measured data with the

predicted values in Figs. 8 and 9 shows that the predictive performance of the network is reasonably good despite the large variations in the data. This comparison also indicates the flexibility and

interpolation/extrapolation performance of the developed network

for predicting gob gas venthole production performance.

The discussions in this and previous sections show that complex

relations exist between different factors and the production performance of gob gas ventholes. In fact, these relations are too complex to

be explained by simple polynomial relations or statistical methods.

The encouraging results obtained in this study suggest that an ANN

approach may be a good candidate for predicting gob gas venthole

production and the percentage of the methane. In that respect, the

flexibility and predictive capability of trained ANNs offer advantages

in decision making and in design of gob gas venthole systems.

5.4. Sensitivity analysis of GGV performance parameters to the inputs

Sensitivity analysis provides a measure of the relative importance

among the inputs of the ANN model and illustrates how the model

output varies in response to variation of an input (Sokolova et al.,

2006). During sensitivity analyses, the weights determined during

network training are not changed. Primarily, the network inputs are

shifted around their mean values and the corresponding changes in

the output are determined. In the NeuroSolutions software, the

activation control component generates the input data for the

sensitivity analysis by temporarily increasing the actual input by a

small value and the corresponding change in output is the sensitivity

data.

In this study, each input channel shown in Fig. 7 was varied around

its mean± 1 standard deviation, while all other inputs were fixed at

their respective means. The resulting total gas flow and methane

percentage for every input were computed for 15 steps above and

below the mean value of the varied input. This process was repeated

for each input to determine the effects of changes in each input on the

network output. Fig. 10A–D shows, as examples, the sensitivity of

predicted total gas rate and methane concentration computed by the

network as a response to changes in overburden depth, casing

diameter, distance of the venthole to the tailgate of the panel and

average vacuum at the wellhead of the venthole, respectively. These

figures show that increases in overburden (Fig. 10-A) decrease gas

flow rate, in accordance with the field observations of Karacan and

Goodman (in press). On the other hand, this change increases

Table 3

Performance of the network when cross validation set was used to test the predictive

capability.

Performance indicator Total flow m3

/day Percent methane

Nominal MSE 0.08494 0.07493

Mean abs error 1139.55 3.80171

Min abs error 2.09183 0.03078

Max abs error 8047.18 24.7915

R 0.957 0.962

Table 4

Predictive performance of the network when testing data set was used.

Performance indicator Total flow m3

/day Percent methane

Nominal MSE 0.13569 0.08595

Mean abs error 1235.92 3.52427

Min abs error 4.72665 0.02904

Max abs error 20,628.04 24.2980

R 0.930 0.956

Fig. 8. Actual versus predicted total flow rates of the gob gas ventholes obtained from

the ANN model shown in Fig. 7.

Fig. 9. Actual versus predicted methane concentrations in the gas production of gob gas

ventholes obtained from the ANN model shown in Fig. 7.
Fig. 10. Response of the ANN outputs (flow rate and methane concentration) to changes in overburden depth (A), casing diameter (B), distance of the venthole to tailgate entries (C) and to applied vacuum at the wellhead (D).
methane percentage in the produced gas, possibly due to increase of

gas content with depth. Increasing casing diameter (Fig. 10-B) has a

more pronounced effect on increasing gas flow potential. However,

diameter increase slightly decreases methane concentration, which may

be due to producing more of the mine air, as explained in Karacan et al.

(2007). Distance of the venthole to panel tailgate (Fig. 10-C) changes

both production efficiency and methane concentration. Increasing

distances from the tailgate decreases gas flow rate due to ineffective

fracturing and movement away from the higher permeability zone

under tension. Methane percentage on the other hand increases slightly

since the atmosphere towards the middle of the panel may be richer in

methane due to lack of ventilation. Fig. 10-D presents how flow and

methane concentration are affected as a function of applied vacuum at

the wellhead of a gob gas venthole. According to the sensitivity results,

application of more suction by the exhausters at the wellhead results in

higher flow rates, whereas methane concentration may decrease due to

capturing more methane air from the gob. This is in agreement with Ren

and Edwards (2002) who reported that a higher suction pressure has

a positive effect of gas capture but this can be offset by diluting the

produced gas with mine air.

Figs. 11 and 12 show the cumulative sensitivity data for total gas

flow obtained from gob gas ventholes and methane concentration in

the gas as a response to variations in the input variables. In these

graphs, the sensitivity values were reported as standard deviations of

the output when varying the inputs about their means. The

cumulative sensitivities shown in Figs. 11 and 12 indicate which of

the input variables are more influential on predicted total gas flow and

methane percentage produced from the gob gas ventholes.

In Fig. 11, it is apparent that percentage of the panel that has been

mined, surface elevation of the ventholes, casing diameter, distance to

tailgate, distance of the venthole to the start of panel and panel length

are more influential on total gas production compared to other input

variables. However, for methane percentage in the total gas produced

(Fig. 12), distance of the venthole to the start of the panel, whether or

not panel is completed, and whether or not the face is advancing seem

more important. The reason for the latter two can be explained as

follows. If the panel is completed, then the expansion of the fractured

reservoir and generation of extensive methane from the overlying

strata will stop or diminish. Methane concentration in the produced

gas will decline more rapidly, giving rise to a higher standard

deviation in sensitivity analysis. A stoppage in face advance has a

similar effect on methane percentage. If the face stops for a long time,

then methane percentage in the produced gas will drop quicker. This,

again, increases standard deviation in the sensitivity data.

Fig. 11. Cumulative sensitivity analysis diagram for total gas flow produced from gob gas ventholes.

Fig. 12. Cumulative sensitivity analysis diagram for methane percentage in the total gas produced from gob gas ventholes.
Table 5 shows the sensitivity values presented in Figs. 11 and 12, as

well as the total sensitivity in standard deviations about the mean due

to sensitivities of gas flow rate and methane percentage to inputs

when they are considered together. The average total sensitivity was

calculated as the arithmetic average of third data column in Table 5,

which gave a value of 33.8. For the sake of this analysis, the total

sensitivity values above this average of 33.8 were considered as high

sensitivities of gob gas venthole performance to the corresponding

input variables. According to this approach and the data in Table 5, (a)

face advance (whether it is advancing or not), (b) percentage of the

panel that has been mined, (c) surface elevation of the venthole

(above sea level), (d) casing diameter, (e) distance of the venthole to

the tailgate, (f) distance of venthole to panel start and (g) panel length

are more influential for the venthole performance. This may raise the

question whether less important parameters based on this sensitivity

analyses can be dropped from the inputs to make the computation

time less, and to reduce the complexity of the problem. The answer to

this question is no: not with this type of analysis. However, if PCA

(principle component analysis) or ANOVA (analysis of variance) is

used, the relations between inputs and outputs, as well as the

relations between inputs can be revealed more precisely and a model

simplification can be done. However, this will be at the expense of

precision, since all of the variance will not be represented with

reduced number of inputs. For a detailed discussion on this, please see

(Karacan, 2008a,b, 2009) and the references therein.

6. Summary and conclusions

Prediction of gob gas venthole production is complex due to the

various factors affecting its performance and the lack of practical

mathematical models that can be used to predict performance. The

objective of this work was to evaluate various factors affecting performance and to develop an artificial neural network (ANN)-based model

for forecasting gob gas venthole productions (gas flow rate and methane

percentage). Sensitivity analyses were also performed to determine the

most influential variables that affect venthole performance.

The total gas productions and methane percentages of 10 ventholes

located over 3 adjacent panels in SW Pennsylvania section of Northern

Appalachian basin were monitored at the wellhead for more than

2 years during and after mining of these panels using pressure, flow,

temperature and methane sensors. The measurements were combined

with various spatial parameters related to venthole location, borehole

completion parameters, mining rate and panel completion data, and

exhauster operation to form an extensive database. A 2-hidden layer

MLP-based ANN model was developed and optimized for the

prediction of total gas flow rate and methane percentage as performance parameters of gob gas venthole production. The ANN model

was successful in predicting those parameters with a correlation coefficient, R, of N0.9.

Sensitivity analyses about the mean of the input variables were

conducted using the ANN model to identify which input variables had

more influence on the performance of gob gas ventholes. The results

of these analyses showed that for total gas flow rate (a) percentage of

the panel that has been mined, (b) surface elevation of the ventholes,

(c) casing diameter, (d) distance to tailgate, (e) distance of the

venthole to the start of panel, and panel length were most influential.

However, for GGV methane percentage (a) distance of the venthole to

the start of the panel, (b) whether or not panel is completed, (c)

whether or not face is advancing were most important. When

considering the overall performance of gob gas ventholes for both

flow and methane percentage, (a) whether or not face is advancing,

(b) percentage of the panel that has been mined, (c) surface elevation

of the venthole (above sea level), (d) casing diameter, (e) distance of

the venthole to the tailgate, (f) distance of venthole to panel start, and

(g) panel length were important variables.

The presented approach using input data evaluation, ANN

modeling, and integrated sensitivity analyses is currently one of the

most systematic and accurate methods to predict production

performance of gob gas ventholes. However, such an approach usually

requires an extensive data set of high confidence. By incorporating

other parameters that may be critical for gob gas venthole production,

this approach and the model can be further improved.

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