Development and application of reservoir models and artificial

neural networks for optimizing ventilation air requirements

in development mining of coal seams

C. Özgen Karacan ⁎

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

Received 28 August 2006; received in revised form 18 December 2006; accepted 10 February 2007

Available online 20 February 2007

Abstract

In longwall development mining of coal seams, planning, optimizing and providing adequate ventilation are very important

steps to eliminate the accumulation of explosive methane–air mixtures in the working environment. Mine operators usually try to

supply maximum ventilation air based on the capacity of the system and the predicted need underground. This approach is neither

economical nor safer as ventilation capacity may decrease in time depending on various mining and coalbed parameters. Thus, it is

important to develop better engineered approaches to optimize mine ventilation effectiveness and, therefore, to ensure a safer work

environment.

This study presents an approach using coalbed methane reservoir modeling and an artificial neural network (ANN) design for

prediction and optimization of methane inflows and ventilation air requirements to maintain methane concentrations below

statutory limits. A coalbed reservoir model of a three-entry development section, which is typical of Pittsburgh Coalbed mines in

the Southwestern Pennsylvania section of Northern Appalachian Basin, was developed taking into account the presence and

absence of shielding boreholes around the entries against methane inflow. In the model, grids were dynamically controlled to

simulate the advance of mining for parametric simulations.

Development and application of artificial neural networks as an optimization tool for ventilation requirements are introduced.

Model predictions are used to develop, train, and test artificial neural networks to optimize ventilation requirements. The sensitivity

and applications of proposed networks for predicting simulator data are presented and discussed. Results show that reservoir

simulations and integrated ANN models can be practical and powerful tools for predicting methane emissions and optimization of

ventilation air requirements.

Published by Elsevier B.V.

Keywords: Underground mining; Coalbed reservoir modeling; Artificial neural networks; Mine ventilation; Reservoir simulation; Mine safety

1. Introduction

In development mining, continuous advance of mine

workings may cause variations in the amount and rate of

methane emissions into the mine atmosphere, which

leads to ventilation requirements continuously changing

to maintain gas levels below legal limits. If adequate

ventilation is not provided, this condition can result in

elevated methane concentrations, which can increase the

risk of an explosion or a frictional ignition that may lead

to an explosion. In order to improve the safety of

underground coal mines, it is important to have the

predictive capabilities to estimate methane emission ⁎ Tel.: +1 412 386 4008; fax: +1 412 386 6891.

E-mail address: cok6@cdc.gov.

0166-5162/$ - see front matter. Published by Elsevier B.V.

doi:10.1016/j.coal.2007.02.003
rates based on coalbed and mining parameters at a

particular stage in mining and to be able to optimize the

mine ventilation requirements.

In addition to improved worker safety, optimization

of ventilation air requirements can also provide a cost

benefit. Historically, mines tended to supply a fixed

volume of air based on the projected maximum demand

(Hardcastle et al., 1997). In today's economic environment, ventilation can consume 30–40% of the electricity

used in underground coal mining operations. It is important to optimize the ventilation requirements to reduce the

ventilation costs in underground coal mines.

As development mining progresses, entry length and

the number of cross cuts (leakage) increase, requiring

additional airflow to adequately ventilate the work

areas. Furthermore, as development lengths increase, it

may become increasingly difficult to keep methane

levels under statutory limits by ventilation alone. One of

the most effective approaches to drain excessive

methane from the coalbed before mining starts and to

shield the entries against methane inflow (Brunner et al.,

1997; Noack, 1998; Diamond, 1994) is to drill

horizontal boreholes in the coalbed. This approach has

been proven to be very effective. Horizontal drilling

technique and its application to degasify coal seams are

documented in the literature (Thakur and Davis, 1977;

Thakur and Poundstone, 1980; Thakur, 1997). However, no predictive techniques and guidelines exist that

establish the distance between the entries and horizontal

wells for optimal shielding and to detail the timing

between well drilling and development mining for

optimal degassing of the coal.

It is important to improve predictive and optimization

methods to provide adequate ventilation air based on the

coalbed and mining parameters. Over the years,

reservoir-modeling methods and simulators have been

developed that can realistically represent the complex

physics of reservoir flow mechanisms in coalbeds and

gas production operations with diverse well completions

(King and Ertekin, 1991). These simulators have been

successfully applied in various coal basins for coalbed

gas recovery using both vertical and horizontal boreholes (Ertekin et al., 1988; Zuber, 1998; Young et al.,

1993). These models offer advanced predictive capabilities to simulate the development mining process and the

prediction of methane inflow rates (Zuber, 1997) as well

as the subsequent determination of airflow requirements

based on coalbed and mining parameters. However, as

the number of independent variables increases, model

solution and analysis become increasingly difficult.

Artificial neural networks (ANNs), on the other

hand, are adaptable systems that can determine relationships between different sets of data. ANNs have been

developed to solve problems where conventional

computer models are inefficient. These problems are

either non-polynomial types having no polynomial

relationship or very complex problems that are difficult

to describe mathematically. The key advantages of

neural networks are their abilities to learn, to recognize

patterns between input and output space, to generalize

solutions, and to interpret incomplete and noisy inputs.

Statistical techniques such as multiple regression

analysis have been used widely for these kinds of

problems, but they often fail in accuracy of prediction,

especially in the face of highly non-linear relationships

or incomplete and noisy data. Owing to the massively

parallel, distributed processing nature of ANNs that can

improve their ability through dynamic learning, they

may outperform other methods of prediction and

optimization. Due to their inherent capabilities and

flexibilities, artificial neural networks are a powerful

predictive and optimization method when used in

conjunction with reservoir simulation methods for

predicting methane inflow and optimizing ventilation

requirements during development mining.

2. Objective and description of the study

This paper presents the development and application

of reservoir simulation and artificial neural network

models for improved prediction and optimization of

methane inflow rates and ventilation requirements

during development mining sections in coal seams.

The reservoir models were developed based on a typical

three-entry Pittsburgh Coalbed mine operating in the

Southwestern Pennsylvania section of the Northern

Appalachian Basin. These models were constructed

with and without the presence of degasification and

shielding against methane. They were developed using

Computer Modeling Group's (CMG, 2003) compositional reservoir simulator (GEM). The models were run

“dynamically” to simulate advance of entry development using a “restart” approach (Karacan et al., 2005, in

press). The reservoir model predictions were compared

with the in-mine monitoring data of air quality and flow

rate obtained during tailgate and headgate entry

development around a longwall panel. Various mining

and degasification-related parameters were considered

for parametric runs using this reservoir simulator.

The “synthetic” cases generated while performing

reservoir simulation runs were used to develop, train,

and test the artificial neural network (ANN) models,

which were developed using Neurosolutions 5.0 software

(NeuroDimensions, 2006). The proposed ANN models

222 C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239
were evaluated for their learning and predictive performances of reservoir simulation results.

3. Reservoir-modeling approach to simulate

development mining

3.1. The mining site monitored for model comparisons

For modeling development mining, a three-entry

tailgate and headgate development, typical of coal mines

operating in Pittsburgh Coalbed in the Southwestern

Pennsylvania section of the Northern Appalachian

Basin, was analyzed (Fig. 1).

During the mining of headgate and tailgate entries,

ventilation air qualities and flow rates were measured by

the operating mining company using methanometers

and flow meters at the monitoring locations shown in

Fig. 1. Methane inflow rates were quantified based on

the measured data. The data was reported as average

monthly concentrations of methane, airflow rate, raw

and clean tonnages of produced coal, and total linear

distances advanced during mining. The length of each

entry section was around 11,000 ft (3353 m) and took

8–9 months to mine.

3.2. Coalbed reservoir and modeling parameters

The base model for formulating development mining

was a coalbed methane reservoir model. A single-layer

coalbed reservoir model was created in Cartesian

coordinates to model fluid flow in the unmined sections

of the coalbed and in the entries, as well as to simulate

development mining operation. In the development of

the model, the parameters and their average values in

Table 1 were used for the Pittsburgh Coalbed. The data

for pre-mining reservoir properties were gathered from

previous NIOSH publications, external reports, personal

communications with the operating mining company,

and previous history matching studies (Karacan et al.,

2005, in press).

In the Pittsburgh Coalbed in the Central and Northern

Appalachian Basin, face and butt cleats are perpendicular and parallel, respectively, to the fold axis. At the

study mine, face cleats were oriented in the E–W

direction and butt cleats in the N–S direction. This

information indicates that the direction of mine advance

is parallel to the face cleats and perpendicular to butt

cleats. When positioning the simulation grids and

assigning the permeabilities, attention was given to

cleat directions.

The gas content and adsorption related data for the

Pittsburgh Coalbed was obtained from the results of

methane adsorption and direct method of gas content

determination tests on various coal samples (Diamond

et al., 1986) and from an unpublished report of Langmuir

parameters of a site-specific core. The spatial distributions of fracture permeabilities of the Pittsburgh Coalbed

have not been previously established or reported. However, based on a large scale modeling study involving

estimation of some coalbed parameters (Karacan et al.,

2005, in press), the average permeabilities for the

Pittsburgh Coalbed were estimated to be 4 md in the

Fig. 1. The modeled long wall panel showing entry sections where mining and ventilation data was gathered during development mining.

Table 1

Values of some of the reservoir parameters of Pittsburgh Coalbed used

in the models

Parameter Value

Permeability-face cleat (md) 4

Permeability-butt cleat (md) 1

Effective porosity (%) 4

Effective fracture (cleat) spacing (ft)/(m) 0.1/0.03

Langmuir pressure (psi)/(MPa) 326/2.25

Langmuir volume (scf/ton)/(cc/g) 490/15.5

Desorption time (days) 20

Initial water saturation (%) 60

Coal density (lb/ft3

)/(g/cc) 84.7/1.35

Pressure (psi)/(MPa) 90/0.61

C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239 223
face cleat direction and 1 md in the butt cleat direction. In

the simulations, these values were taken to be uniform

throughout the layer.

The gas relative permeability curve of the coalbed

was estimated by matching the average gas production

rate from the simulated boreholes in the models to the

reported average methane production rates of three

horizontal degasification boreholes in the mining area

[8.42 scf/day/ft (0.782 m3

/day/m) vs. 8.70 scf/day/ft

(0.807 m3

/day/m)]. A similar field-data-based adjustment could not be made to the water relative

permeability curve because no data was available.

Thus, the curve was set to low values so the wells

would experience low amounts of water [0.0016 bbl/

day/ft (0.0015 m3

/day/m)].

3.3. Modeling of development mining process and

performing the simulations

Various factors control the ventilation requirements

during development mining. Among these factors,

coalbed parameters and mining parameters are probably

the most important. In this study, the Pittsburgh Coalbed

was selected as the primary coalbed of interest and its

average properties (Table 1) were used in the models.

Thus, the variables originating from coalbed reservoir

properties were mostly eliminated from parametric runs

and subsequent analyses. In that sense, these models

may be considered site-specific to Pittsburgh Coalbed.

However, the effects of coalbed parameters can be

investigated easily using the same approach to increase

the predictive capability for other coalbeds. The main

areas of emphasis were mining parameters, the methane

concentration level to be maintained, and the presence

or absence of a degasification/shielding program to

reduce the amount of methane emissions into the

developed entries. Based on this approach, two different

types of models, with and without horizontal degasification wellbores, were developed and simulation runs

were performed. The parameters and their ranges of

values are shown in Table 2.

For modeling driveage of tailgate and headgate

entries, a three-entry development model around a

longwall panel was studied. Fig. 2 shows a snapshot of

the model that represents mining advance, pillar layout,

and the ventilation scheme. An oblique plane was

removed from the picture for a better visualization of the

different elements of the model. The middle entry was

modeled as the “track” or haulage entry, where intake air

was entering. In this entry, ventilation air injection and

mine pressure assignments to the grids were performed

using an injector well. The entry to the left of “track”

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

designated as the “return” entry carrying away majority

(N90%) of the methane emissions and the methaneloaded ventilation air. The producer wells used in these

entries both assigned the mine pressures and monitored

the amount of methane in the produced gas stream. This

was the amount of methane entering the entries as

mining progressed and was used to calculate ventilation

requirements to dilute it to desired levels at any stage in

mining.

During development mining, the entries and the cross

cuts constituting the volume to be ventilated are

continuously extended imposing a moving boundarytype problem in modeling. As the continuous miner

advances, new volumes are created that must be

ventilated while new surfaces are created that liberate

gas into that volume. In this study, the development of a

three-entry continuous mining model, shown in Figs. 2

and 3, was handled using “restart” model runs. These

models were run sequentially, each characterizing an

advance in entry development with a specified development rate where coalbed properties were replaced

with the assigned properties of the entries in the models

and ventilation-related features are built. The simulation

Table 2

The parameters and their range of values changed in simulation runs

Parameter Range

Mining height (ft)/(m) 5–7/1.52–2.13

Entry length (ft)/(m) 1000–12,000/305–

3658

Mining rate (ft/day)/(m/day) 25–175/7.6–53.3

Methane concentration in mine air (%) 0.5–1.5

Distance of shielding wells to entries (ft)/(m) 19–87/5.8–26.5

Degasification duration before mining (days) 0–180

Fig. 2. A three dimensional snapshot of pressure distribution during

development of the coalbed. Pillars, ventilation scheme and the

modeled shielding wells are also shown.

224 C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239
outputs from the previous model run were written in a

“restart” file and then used by the next model as the

input run, while reservoir parameters were changed to

characterize the development of entries. This approach

was used recently by Karacan et al. (2005, in press) to

model the longwall mining process and the performances of gob gas ventholes. Zuber (1997) approached

the problem of roadway development by using a

reservoir simulator and modifying the grids at appropriate times at the differential equation level without

restarting the simulation.

In building the restart models, all three entries were

developed simultaneously to a specific distance in a

predefined amount of time, allowing calculation of the

Fig. 3. (A–B) A portion of the grid model showing the advance of mining at two successive stages and the resultant ventilation airflow paths (arrows).

C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239 225
rate of mining advance. For different mining rates, the

times in the recurrent data set were changed. Thus, the

rates reported in this study are not linear mining rates,

but instead represent the rates of the mine section

advance. Based on this approach, a 150 ft (45.7 m)

section advance corresponds to 570 ft (173.7 m) of

linear mining distance in the model, including entries

and cross cuts.

As the entries were developed at the designated rates

in the models by changing coalbed properties, the pillars

and cross cuts were also developed at the same time. The

pillars between the entries were 125 ft (38.1 m) in length

and 75 ft (22.9 m) in width, and had the same properties

as the coalbed. However, during the development of

entries, each of which was 20 ft (6.6 m) in width and the

same height as the coalbed, the permeability was

replaced with a high permeability [109 md (10−6 m2

)]

in all three directions. Also, the coal-matrix pressures in

the mined grids were assigned to atmospheric pressures

to simulate mining process.

The development of cross cuts was modeled the same

way as the entries. However, during each simulation run

only the last set of cross cuts was left fully open for

ventilation airflow. During the development of cross

cuts, stoppings or block walls (between track-belt and

track-return) were automatically created between the

restart runs to force ventilation flow through the last

cross cuts at each section advance. A “curtain” resistance in the last section of the “track” diverted some of

the intake air towards the “belt” entry to ventilate both

belt and face (Fig. 3A and B). A permeability of 100 md

(10−13 m2

) was assigned to the stoppings to represent

leakage, a common occurrence in underground mining.

Fig. 3A and B shows the progress of mining between

two successive steps. These figures show the entries,

cross cuts, and the pillars created during simulations

plus the path of the ventilation airflow.

In this study, two different modeling approaches

were undertaken to predict methane inflows and to

estimate ventilation requirements during development

mining: with and without degasification wellbores

around the entries to shield them from migrating

methane. The variables of these models were mining

rate, mining height (coalbed thickness), methane percentage in the ventilation air, length of developed

entries, pre-mining degasification duration and the

proximity of boreholes to the entries, as summarized

in Table 2. In the models with shielding boreholes,

the borehole diameters were 3 in. (7.6 cm), with no

wellbore skin, and they were operated with −0.2 psia

(−1260 Pa) bottom-hole pressure. The lengths of the

boreholes were equal to the lengths of the entries.

The wellbores were operated during mining regardless of the duration of the pre-mining degasification

period.

4. Analysis of reservoir model predictions of

methane inflow and airflow

4.1. Description of mining conditions and parameters

in the monitored mine and comparisons with model

predictions

During mining of tailgate and headgate entries shown

in Fig. 1, various parameters of mining and ventilation

were measured by the operating mining company. The

linear advance distances, lengths of the exposed ribs, as

well as raw and clean coal tonnages were reported as

monthly totals. The measured air quality, ventilation

airflow rates, and calculated methane inflow rates were

reported as monthly averages.

The mining distances were reported as linear distances mined (including entries and cross cuts), which

were around 4500–6500 ft (1372 m–1981 m) per

month, rather than the daily advance rate of all three

entries in the direction of mine advance as formulated in

the model. In order to establish a comparison between

measured data and the reservoir model formulation,

linear mining distances were converted to net advance of

entries. This approach revealed a ratio of 0.27 (1 section

length = 3.7 linear length) between these two length

scales. Thus, the linear mining distances were converted

to distances in the direction of mine advance. The

advance rates, using the lengths in the direction of mine

advance, were calculated based on two eight-hour

production shifts and one maintenance shift per day

for 6 days/week. The majority of calculated section

advance rates were between 70–110 ft/day (21.3–

33.5 m/day) during production shifts and working

days. The average rate of mining until both entry

sections were developed to full length was approximately 80 ft/day (24.4 m/day).

The Pittsburgh Coalbed generally has a uniform

thickness of about 7 ft (2.1 m), although it can vary

from 6–8 ft (1.8–2.4 m) in the studied region. These

regional variations may affect methane inflow rate

into the mine and the methane concentration in the

ventilation air. The coalbed thickness information was

not included in the data sheet of the monitoring study.

Thus, raw tonnages and the average dimensions of the

entries and cross cuts were used to convert this information into mining height. The results indicated

that the average thickness of the coalbed varied from

6.2–7.6 ft (1.9–2.3 m) in the direction of mining. The

226 C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239
average thicknesses of the coalbed along the tailgate and headgate sections were 6.9 ft and 6.8 ft,

respectively.

Changes in reported average methane concentration

during monitoring were caused by changes in mining

rates, mining height, and ventilation rates. The measured

methane concentration in the mine atmosphere increased from 0.25% at the beginning of the headgate

and tailgate development to 0.85% at the end. The

averages over the entire mining period were 0.56% and

0.57% for tailgate developments and headgate developments, respectively. However, it should be noted that the

ventilation airflow increased from 44,000 cfm (ft3

/min)

to 70,000 cfm (20.76 m3

/s and 33.04 m3/s) measured at

monitoring points shown in Fig. 1 during this same

period (Tables 3 and 4). Although the mine was

successful in maintaining methane levels below the

1% statutory limit for the whole mining period,

increasing methane concentrations suggest that the

increase in methane inflow rate offset the increase in

ventilation airflow. To maintain a constant, low methane

concentration, the ventilation airflow should have

increased as the length of the developed entries

increased. Tables 3 and 4 show the measured and

calculated parameters discussed above for mining of

tailgate and headgate entries in Fig. 1.

4.2. Comparison of reservoir model prediction with inmine monitoring data

The predictive performance of the reservoir model

was compared with in-mine measurements of methane

inflow, reported as monthly averages, during development mining of the tailgate and headgate entries. Since

there was no report indicating that the area was

degasified using horizontal wells, reservoir models

developed without the degasification option and their

outputs of predicted methane inflow and ventilation

airflow rates were compared with the measured data.

The reservoir models utilized mining parameters that

were close to the calculated averages of monitored

variables, i.e., 7 ft (2.1 m) mining height, 70 ft/day and

110 ft/day (21.3 m–30.5 m) advance rate, and 2000–

12,000 ft (610–3658 m) development length. Airflow

rates were calculated to produce a constant 0.5%

methane concentration based on methane inflow

predictions.

Fig. 4 shows the simulator predictions and measured

methane inflows into the headgate and tailgate entries.

The solid markers and the trendlines show the simulator

predictions for methane emissions at two different

mining rates, upper and lower limits of the calculated

average advance rate of the mining, as a function of

Table 3

Measured (at the monitoring point in return entry shown in Fig. 1) and calculated parameters for mining of tailgate entries

Mining duration

from start (months)

Airflow rate (cfm)

— measured

Methane concentration

(%) — measured

Mining advance rate (ft/

day) — calculated

Length of mined entries from

start (ft) — calculated

Coal thickness (ft)

— calculated

1 44,425 0.250 103 1758 6.9

2 55,533 0.200 100 3468 7.3

3 52,965 0.425 77 4793 7.6

4 60,239 0.500 84 6222 6.9

5 67,850 0.675 42 6939 7.4

6 64,548 0.750 83 8358 6.5

7 64,725 0.925 75 9639 6.2

8 71,280 0.825 98 11,311 6.4

Table 4

Measured (at the monitoring point in return entry shown in Fig. 1) and calculated parameters for mining of headgate entries

Mining duration

from start (months)

Airflow rate

(cfm)-measured

Methane concentration

(%) — measured

Mining advance rate

(ft/day) — calculated

Length of mined entries from

start (ft) — calculated

Coal thickness (ft) —

calculated

1 47,830 0.425 80 1360 6.7

2 47,949 0.750 78 2697 6.6

3 45,530 0.475 63 3781 6.3

4 49,445 0.325 20 4131 6.8

5 53,065 0.433 90 5676 7.1

6 43,915 0.600 96 7319 6.7

7 63,540 0.725 89 8848 6.5

8 69,120 0.850 109 10,701 7.6

C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239 227
entry lengths. Since the simulations use constant mining

and coalbed parameters, the methane inflow results

show a continuous increase with the length of mined

entries, as opposed to the scattered increase in measured

data. However, the developed model closely predicts the

range of measured data.

The predicted airflow rates needed to maintain a

constant 0.5% methane level in the ventilation air and

the measured airflow rates that resulted in an average

methane concentration of 0.57% are shown in Fig. 5.

This figure shows that, because of methane inflow

increases, simulator predictions of air requirement

increase with entry length to keep the methane levels

at 0.5%. Although the average predicted airflow rate is

close to the average of the measured values, the

measured values at the start and end of the mining are

higher and lower than the predicted, respectively. The

model calculates the required amount of air based on the

simulated methane inflow. During mining, the ventilation air rate was kept in a narrow range regardless of the

length of the developed section or the mining rate. In

fact, methane levels increased from 0.20% at the

beginning of development to 0.85% at the end of

development (Tables 3 and 4), indicating an abundance

of ventilation air at the beginning and a lack of airflow at

the end. This comparison suggests that adjusting airflow

quantity based upon mining progress using a predictive

method may be safer and more economical than

Fig. 4. Comparison of methane inflow calculated in the mine based on measurements of airflow rate and air quality and the simulator predictions at

two different mining rates.

Fig. 5. Comparison of airflow rate measured in the mine and the simulator calculation based on two different mining rates to maintain a constant

methane level of 0.5%.

228 C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239
supplying a fixed volume of air based on projected

maximum demand.

4.3. Parametric analysis of reservoir-modeling results

The reservoir model was used to generate parametric

simulations to cover a range of values for the miningrelated parameters shown in Table 2. Some of those

results, which later were used to develop an artificial

neural network (ANN) prediction and optimization tool,

are presented in this section to show the relationships

and effects of different parameters on methane inflow

into the entries during development mining.

The reservoir models predicted the methane inflow

rate into the entries. Once the methane inflow rate is

known, the airflow requirements can be calculated to

keep methane concentrations at a required level. Fig. 6

shows the predicted methane inflow rates at 2000, 8000,

and 12,000 ft (610 m, 2438 m, and 3658 m) of roadway

development in the 7-ft-high Pittsburgh Coalbed with

different development rates. Results show that the

methane inflow rate increases with the length of the

development section because of the increase in surface

area of the exposed coalbed. This increase is a strong

function of the mining rate. However, the effect of the

mining rate on methane inflow is less pronounced for

shorter development distances than longer distances.

For 2000 ft of roadway development, increasing the

mining rate from 25 ft/day (7.6 m/day) to 175 ft/day

(53 m/day) increases the methane inflow rate from

109,100 scf/day (3091 m3

/day) to 214,000 scf/day

(6062 m3

/day), which is approximately a two-fold

increase. With an 8000 ft entry development, after

increasing the mining rate from 25 ft/day to 175 ft/day,

Fig. 7. Calculated airflow rates required to maintain various methane concentrations for different mining rates and entry lengths.

Fig. 6. The change of methane inflow rates predicted by the reservoir simulation as a function of the mining rate for different entry lengths.

C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239 229
the methane inflow increases from 244,300 scf/day

(6921 m3

/day) to 700,500 scf/day (19,850 m3/day).

Similarly, increasing the mining rate from 25 ft/day to

175 ft/day in developing 12,000 ft of entries increases

methane inflow rate from 309,000 scf/day (8755 m3

/

day) to 991,150 scf/day (28,079 m3

/day).

The methane inflow simulations performed for

development mining can be used for evaluating and

designing ventilation requirements. Fig. 7 shows the

calculated airflow requirements as a function of the

mining rate to keep methane concentrations at predetermined concentrations during development of 2000

and 12,000 ft (610 m and 3658 m) entry sections. For

this calculation, the methane inflow rates shown in

Fig. 6 were used. With shorter development distances,

only minimal adjustments to ventilation airflow are

needed to keep methane levels below 1%. Such adjustments appear to be independent of the mining rate.

With longer development distances, more ventilation

airflow is needed to keep methane levels constant and

the amount is more strongly affected by the mining rate.

If the ventilation rate cannot control methane levels,

decreasing the mining rate can be considered as a

desired control.

Fig. 8. Methane inflow rate predictions for different development lengths and various pre-mining degasification durations. “No degasification” in the

figure legend corresponds to the case where there was no degasification boreholes. On the other hand, 0 months pre-mining degasification and

methane production from the wells starts at the same time as the mining. Data represents a development rate of 70 ft/day (21.4 m/day) and horizontal

boreholes located 19 ft (5.8 m) from the entries.

Fig. 9. Effect of degasification duration on the methane inflow rate for various development lengths and rates. “No degasification” in X-axis

corresponds to the case where there were no degasification boreholes located 19 ft the entries. Different months are the pre-mining degasification

durations using boreholes located 19 ft from the entries.

230 C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239
Using in-seam horizontal boreholes is an effective

approach to shield entries against methane inflow during

development mining. To simulate the shielding effects

of degasification wells on methane inflow and on the

ventilation air requirements during development mining, pre-mining degasification periods of 0 months (i.e.,

no pre-mining degasification) and 3 and 6 months were

modeled. The boreholes were placed 19–87 ft (5.8–

26.5 m) away from the entries for parametric analysis. In

all cases, the wellbores were operated during mining

regardless of the duration of pre-mining degasification.

Fig. 8 shows a comparison of methane inflow rates as

a function of development length for horizontal boreholes located 19 ft (5.8 m) away from the intake and

return entries and operated for various pre-mining

durations. The data represents a 70-ft/day (21.4 m/day)

development rate in a 7-ft (2.1-m) thick coalbed.

Simulations show that the methane emissions are highest

when shielding is not used against methane inflow

before or during mining. Emission rates progressively

decrease as a result of shielding and degasification. For

instance, even if the boreholes are not operated before

mining and begin to operate when mining starts, the

inflow rate decreases about 25% compared to the

completely unshielded case. The methane inflow rates

after longer pre-mining degasification times are less.

Fig. 9 shows the effect of mining rate and pre-mining

degasification duration when mining development

Fig. 10. Effect of proximity of shielding wells to the entries and the length of entries on methane inflow rate. “No degasification” in the figure legend

corresponds to the case where there were no any degasification boreholes. The other data are after 6 months of degasification. The simulated data

shown is for 70 ft/day (21.4 m/day) development rate.

Fig. 11. Effect of mining rate and shielding well proximity when mining 2000-ft and 12,000-ft (610-m and 3658-m) long entries. Wells operated for

6 months prior to the start of development mining.

C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239 231
entries with lengths of 2000 and 12,000 ft (610 m and

3658 m). The presence of methane shielding and the

duration of pre-mining degasification are more important when development distances are long and mining

rates are high.

Fig. 10 shows the effects of the proximity of the

shielding wells to the entries and the development

length on methane inflow rate. The simulated data

shown is for 70-ft/day (21.4 m/day) development rate in

7-ft (2.1-m) thick coalbed after 6 months of pre-mining

degasification, except for the no-degasification case,

which does not have any shielding. This figure shows

that those wellbores closest to the entries are most

effective in reducing methane inflow rates during

development mining, about 50% at 12,000 ft (3658 m)

compared to no-shielding. Thus, positioning wells as

close to the entries as practically possible serves better

for shielding purposes.

Fig. 11 shows the effect of mining rate and shielding

well proximity for mining 2000-ft and 12000-ft (610-m

and 3658-m) long entries after operating the wells for

6 months prior to the start of development mining.

Operating shielding wells that are located at close proximity to the entries for 6 months prior to mining may

reduce the methane inflow rate into the entries as much

as 50% during development mining, especially when

mining longer entry sections at higher mining rates.

5. Development and application of artificial neural

networks (ANNs) integrated with reservoir simulation

as a prediction and optimization tool for development

mining

5.1. Basic principles and components of an ANN

A neural network is a computing tool that processes

information by its dynamic state response to external

inputs (Kosko, 1992). One of the most widely used

neural network topologies is the multilayer perceptron

(MLP) because of its applicability to different problems

(Hagan, 1997; Schalkoff, 1997). In MLPs, the minimum

number of layers is three, which includes input, hidden,

and output layers. Generally, as the problem gets

complicated, larger and complex ANN topologies may

be required. However, although topologies of this nature

generally will achieve mostly lower errors, this may also

be due to over-fitting rather than good modeling

(Statsoft, 2003).

The total (net) input to a neuron and its output are

calculated using a transfer function, or axon. This is

Table 5

The performance results of different ANN topologies tested to find the suitable network for the development mining simulations without

degasification or shielding boreholes

Hidden

layers

Neuron

per

hidden

layer

Epoch Transfer

function

Momentum Training X-validation Testing

Minimum MSE Final MSE Minimum MSE Final MSE NMSE R

1 1 4 500 Tanhaxon 0.3 0.00462 0.00462 0.00498 0.00498 0.1892 0.90318

2 1 4 500 Tanhaxon 0.5 0.00499 0.00499 0.00621 0.00621 0.2140 0.89137

3 1 4 500 Tanhaxon 0.7 0.00312 0.00312 0.00286 0.00286 0.1142 0.94237

4 1 6 500 Tanhaxon 0.7 0.00193 0.00193 0.00176 0.00176 0.0599 0.96992

5 1 6 1000 Tanhaxon 0.7 0.00064 0.00064 0.00106 0.00106 0.0184 0.99085

6 1 4 1000 Tanhaxon 0.7 0.00184 0.00184 0.00127 0.00127 0.0656 0.96719

7 1 4 2000 Tanhaxon 0.7 0.00059 0.00059 0.00049 0.00049 0.0206 0.98964

8 1 6 2000 Tanhaxon 0.7 0.00029 0.00029 0.00023 0.00023 0.0134 0.99335

9 1 6 1500 Tanhaxon 0.7 0.00062 0.00062 0.00056 0.00056 0.0219 0.98899

10 1 6 2000 Tanhaxon 0.8 0.00046 0.00046 0.00043 0.00043 0.0207 0.98970

11 2 6 2000 Tanhaxon 0.7 0.00035 0.00035 0.00034 0.00034 0.0090 0.85549

12 1 4 500 Sigmoid 0.3 0.01201 0.01201 0.01931 0.01931 0.8952 0.68225

13 1 4 500 Sigmoid 0.5 0.01237 0.01237 0.02040 0.02040 0.9292 0.77537

14 1 4 500 Sigmoid 0.7 0.01003 0.01003 0.01681 0.01681 0.7764 0.80288

15 1 6 500 Sigmoid 0.7 0.00574 0.00574 0.00961 0.00961 0.4895 0.85107

16 1 6 1000 Sigmoid 0.7 0.00407 0.00407 0.00666 0.00066 0.3812 0.84414

17 1 4 1000 Sigmoid 0.7 0.00558 0.00558 0.00937 0.00937 0.4694 0.83881

18 1 4 2000 Sigmoid 0.7 0.00202 0.00202 0.00263 0.00263 0.2690 0.85800

19 1 6 2000 Sigmoid 0.7 0.00171 0.00171 0.00198 0.00198 0.2414 0.87429

20 1 6 4000 Sigmoid 0.7 0.00153 0.00153 0.00167 0.00167 0.2253 0.88322

21 2 6 2000 Sigmoid 0.7 0.01249 0.01249 0.02061 0.02061 0.9434 0.81673

Data rendered in bold are the most suitable topologies in each category, among the schemes tested. All the learning rules are momentum.

232 C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239
sometimes called a “squashing function” (Eberhart and

Dobbins, 1990), since it compresses the output range

between either 0 to1 or −1 to 1, depending on the choice

of the transfer function. While there are various transfer

functions, the hyperbolic tangent axon (Tanhaxon) and

Sigmoid functions are generally the non-linear axons

used.

Based on the training method, neural networks are

classified as either supervised or unsupervised networks (Mohaghegh, 2000). The supervised training

algorithm requires repeated showings (Epoch) of both

input vectors and the expected outputs of the training

set to the network to let it learn the relations on a

feedback basis (Gorucu et al. 2005). The neural network computes its output at each epoch and compares it

with the expected output (target) of each input vector in

order to calculate the error. Minimizing the mean

square error (MSE) is the goal of the training process.

The most widely used technique is to propagate the

error back and adjust the initially assigned random

weights to each neuron. This process is called backpropagation. This is the technique used in the modeling

described in this paper. Training is one of the most

important steps in the development phase of the neural

network, since the weights and the network characteristics will be used later in testing data sets and making

subsequent predictions.

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.

5.2. Reservoir simulations as the source of the input–

output patterns

The “dynamic” reservoir simulation model that

predicted the methane inflows generated “synthetic”

case studies to develop an artificial neural network-based

prediction and optimization tool. Airflow requirements

Table 6

The performance results of different ANN topologies tested to find the suitable network for the development mining simulations with degasification/

shielding boreholes

Hidden

layers

Neuron

per

hidden

layer

Epoch Transfer

function

Momentum Training X-validation Testing

Minimum MSE Final MSE Minimum MSE Final MSE NMSE R

1 1 4 500 Tanhaxon 0.3 0.00412 0.00412 0.00567 0.00567 0.13408 0.93107

2 1 4 500 Tanhaxon 0.5 0.00351 0.00351 0.00518 0.00518 0.11492 0.94124

3 1 4 500 Tanhaxon 0.7 0.00332 0.00332 0.00490 0.00490 0.10858 0.94475

4 1 6 500 Tanhaxon 0.7 0.00211 0.00211 0.00289 0.00289 0.06592 0.96733

5 1 8 500 Tanhaxon 0.7 0.00304 0.00304 0.00469 0.00469 0.10479 0.94635

6 1 6 1000 Tanhaxon 0.7 0.00073 0.00073 0.00113 0.00113 0.03076 0.98499

7 1 6 2000 Tanhaxon 0.7 0.00052 0.00052 0.00085 0.00085 0.02452 0.98845

8 1 6 1500 Tanhaxon 0.7 0.00051 0.00051 0.00074 0.00074 0.02174 0.98990

9 1 8 2000 Tanhaxon 0.7 0.00282 0.00282 0.00457 0.00457 0.09693 0.95086

10 1 8 2000 Tanhaxon 0.8 0.00034 0.00034 0.00059 0.00059 0.01170 0.99420

11 1 10 2000 Tanhaxon 0.8 0.00030 0.00030 0.00075 0.00075 0.02005 0.99069

12 1 10 2000 Tanhaxon 0.7 0.00034 0.00034 0.00067 0.00067 0.01933 0.99095

13 1 10 1500 Tanhaxon 0.7 0.00075 0.00075 0.00082 0.00082 0.01880 0.99108

14 1 8 4000 Tanhaxon 0.7 0.00034 0.00034 0.00073 0.00073 0.02162 0.98959

15 2 6 1500 Tanhaxon 0.7 0.00076 0.00076 0.00115 0.00115 0.03222 0.98523

16 1 4 500 Sigmoid 0.3 0.01524 0.01524 0.01171 0.01171 0.95764 0.61147

17 1 4 500 Sigmoid 0.5 0.01567 0.01567 0.01213 0.01213 0.94738 0.80786

18 1 4 500 Sigmoid 0.7 0.01186 0.01186 0.00900 0.00900 0.73909 0.85221

19 1 6 500 Sigmoid 0.7 0.00899 0.00899 0.00657 0.00657 0.55004 0.86702

20 1 8 500 Sigmoid 0.7 0.00904 0.00903 0.00685 0.00685 0.57277 0.85524

21 1 6 1000 Sigmoid 0.7 0.00467 0.00467 0.00356 0.00356 0.29142 0.89142

22 1 6 2000 Sigmoid 0.7 0.00221 0.00221 0.00184 0.00184 0.14768 0.92487

23 1 8 2000 Sigmoid 0.7 0.00230 0.00230 0.00188 0.00188 0.15540 0.92245

24 1 10 1500 Sigmoid 0.7 0.00258 0.00258 0.00211 0.00211 0.17099 0.91194

25 1 6 1500 Sigmoid 0.7 0.00255 0.00255 0.00211 0.00211 0.17074 0.91233

26 1 6 4000 Sigmoid 0.7 0.00192 0.00192 0.00159 0.00159 0.12652 0.93538

27 2 6 2000 Sigmoid 0.7 0.01506 0.01506 0.01157 0.01157 0.91014 0.83561

Data rendered in bold are the most suitable topologies in each category among the schemes tested. All the learning rules are momentum.

C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239 233
were calculated for required methane concentrations

based on methane inflows predicted by the reservoir

simulation.

For the purposes of developing an ANN tool and

training it as a predictive tool, the reservoir simulation

results were divided into two groups based on the use of

degasification. The reservoir simulations without degasification resulted in four variables, each with its own

range of values, and reservoir simulations with the

degasification option resulted in six variables, shown in

Table 2. The values of the input variables were changed

between model runs to create physically meaningful

simulation data points. Therefore, 360 modeling data

points (exemplars) for the no-degasification case and

720 exemplars for the degasification case were

generated for these two classes of simulations.

5.3. Development of ANN models

The development and implementation of the ANN

were performed using NeuroSolutionsTM version 5.0

software (NeuroDimensions, 2006). Entry developments with and without degasification were considered

separately with two different ANNs constructed for each

case. The whole data set (exemplars) created for each

class of problem was separated into three sections to use

them for training, cross validation, and testing purposes.

In the case of simulations without shielding boreholes,

252 out of 360 exemplars (70%) were saved as training

data, 36 exemplars (10%) were saved as cross-validation

data set, and 72 exemplars (20%) were saved for testing

the trained network. In the case of the simulations with

shielding wells, the number of exemplars separated for

training were 288 out of 720 (40%), 144 (20%) for cross

validation, and 288 (40%) for testing. The criterion in

breaking up the data was to allocate enough exemplars

for the ANN training and testing.

Fig. 13. Comparison of target airflow rates (simulator-based predictions for the case without degasification wells) of the testing data set

with the predicted rates from the proposed ANN shown in Fig. 12A.

Fig. 12. The ANN topologies developed for two different classes of reservoir simulation for the mine ventilation problems studied in the paper. (A)

Prediction of air requirements for ventilating development entries in the absence of any methane degasification and shielding around the entries. (B)

Prediction of air requirements in the presence of degasification and the shielding against methane inflow).

234 C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239
5.3.1. Performance study for the selection of a suitable

ANN topology

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 neurons in hidden layers, the number of

epochs, the type of transfer function, and the value of

momentum were varied. Tables 5 and 6 give the results

of this parametric search to build an ANN for the

analyzed ventilation problems.

Table 5 gives the results of the parametric search for a

suitable network structure function for the ventilation of

entries in the absence of degasification. In this

sensitivity study, a single-hidden layer was predicted

to be adequate, although one case with two hidden

layers was tested as well. The best combination of

network topology and parameter values were obtained

with 6 neurons, 2000 epochs, and 0.7 as the momentum

value using a tanhaxon transfer function. The MSEs

obtained in the training and cross-validation phases

(0.00029 and 0.00023, respectively), and the NMSE

and R (0.0134 and 0.993, respectively) were the best

combinations among the examined networks. The

minimum and maximum absolute errors were 10.3 ft3

/

min (cfm) (0.00486 m3

/s) and 17,400 cfm (8.21 m3/s),

respectively. Sigmoid function was also tested with

several combinations of parameters, but its results were

not as good as the ones obtained with tanhaxon transfer

function (Table 5).

A similar sensitivity analysis was performed with the

data set using the shielding option. Table 6 gives the

results of performances achieved with various combinations of network topology and tested parameters. In this

analysis, 1 hidden layer, 8 hidden-layer neurons, 2000

epochs, and a momentum value of 0.8 gave the best

results for the tanhaxon transfer function tests. This

combination gave low MSEs in training and in crossFig. 14. Distribution of the number of exemplars within certain relative

error bins in testing of the completely new data (testing set) for the case

without degasification wells (Fig. 13) using the network in Fig. 12A.

Fig. 15. Comparison of target airflow rates (simulator-based predictions for the case without degasification wells) of the testing data set

with the predicted rates from the proposed ANN shown in Fig. 12B.

Fig. 16. Distribution of the number of exemplars within certain relative

error bins in testing of the completely new data (testing set) for the case

without degasification wells (Fig. 15) using the network in Fig. 12B.

Fig. 17. Comparison of monthly-averaged airflow rate measured

during development of tailgate and headgate entries shown in Fig. 1

and the ANN predictions based on the calculated input parameters.

C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239 235
validation runs (0.00034 and 0.00059, respectively), a

low nominal mean squared error (0.0117), and a high

regression coefficient in the testing phase (0.9942). The

minimum and maximum errors were 4.39 cfm

(0.00207 m3

/s) and 10,700 cfm (5.29 m3/s), respectively.

Although higher numbers of hidden-layer neurons (10)

were tested with both 0.7 and 0.8 momentum coefficients, the results did not improve. The Sigmoid function

performed less efficiently than the tanhaxon function for

every combination tested (Table 6). Thus, the tanhaxon

was selected as the transfer function for the ANN network

to analyze ventilation with degasification.

The topologies shown in Fig. 12 are proposed as a

tool to analyze and predict ventilation airflow requirements with and without the use of degasification.

5.3.2. Training of the ANN models

Ventilation of underground coal mines is a complex

process and involves many considerations since the

methane gas levels may change. Some of these factors

include reservoir properties of the coalbed, mining

operation-related changes in the methane inflow into

the entries, the length of development entries, and the

presence or absence of any previous degasification or

shielding. In this study, changes in the reservoir properties of the coalbed were excluded from the variables, by

adapting the models for the Pittsburgh Coalbed. Even

then, it is not easy to predict an optimized ventilation

airflow rate to achieve a certain methane level for the

three-entry mine geometry shown in Figs. 1–3. In the

absence of a data library and an appropriate tool to

analyze it, the only choice is to create and run a new

reservoir model, which usually is a lengthy process.

While it is also possible to make some estimates based

on interpolations, that approach is not only time consuming but is usually inaccurate. Ultimately, the ANN is

expected to make accurate estimates for completely new

input parameters, by reproducing the results of these

simulations and approximating the results of a completely new data set.

For training and cross validation of the ANN shown

in Fig. 12A, developed for the case without degasification, 252 training patterns and 36 cross-validation

patterns were shown to the network 2000 times

(epoch) to establish the relationships and minimize the

error. The MSE for training and cross validation of this

network as a function of the epoch showed that the error

began to decrease rapidly for both training and cross

validation. However, the minimum MSEs were obtained

at the end of the 2000 epoch cycles. The final MSEs

obtained were 0.00029 and 0.00023 for training and

cross validation, respectively.

For training and cross validation of the ANN

developed for the case with degasification (Fig. 12B),

288 training patterns and 144 cross-validation patterns

were shown to the network 2000 times as determined

from the ANN performance study (Table 6). The

tanhaxon was selected as the transfer function and 0.8

was used as the momentum factor for this network

having one hidden layer, eight hidden neurons, and six

input neurons. For this network, the minimum and also

final MSEs obtained at the end of the 2000th epoch

cycle were 0.00034 for training and 0.00059 for cross

validation.

5.3.3. Testing of the developed ANN models

The trained networks were tested in two stages. In the

first stage, the networks were tested using the training

data. The same training data set 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 a 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 training set when the same data is used

in the testing phase. Poor predictive performance

towards a new set suggests that the ANN cannot

generalize 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.

In testing of the ANN model without shielding

boreholes (Fig. 12A), the performance of the network

was first tested against the training data set having 252

exemplars. The analysis showed that the airflow rates

predicted by the network were very close to the target

values (simulator-based predictions) in the training set

for the majority of the data set. The minimum and

maximum absolute errors were 1.20 cfm (0.00056 m3

/s)

and 10250 cfm (4.84 m3

/s), respectively. The NMSE

and R values were 0.0043 and 0.9978, respectively,

which shows a good degree of success in the prediction

performance. Using training data set in testing, the

prediction errors revealed that the error generally

fluctuated between +/−5% depending on the airflow

rate variations between predictions and targets, although

it is generally less than 5% for most of the data set.

Distribution of exemplars within certain error bins

revealed that the error for 50 of the exemplars (19.7%)

was between +/−1%. The number of exemplars having

+/−3% error was 129 (51%) in the data set. When an

236 C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239
error margin of +/−5% was considered, 183 (72%)

exemplars fell within this range. Overall, 91% of the

data had an error less than 10%. The average of the

absolute value of relative errors for the entire set of

exemplars was 4.0%.

In the second stage of testing for this ANN (Fig. 12A),

the network was exposed to a new data set (testing set) to

evaluate its predictive performance. For this simulation,

72 exemplars were saved. Fig. 13 shows the comparison

of airflow rates predicted by the network to the target

values of the testing set calculated by the reservoir

simulator. This figure shows that the predictions of the

trained network are close to the simulator predictions for

the completely new data set. For testing with this new

data set, the NMSE and R values were 0.0134 and 0.993,

respectively. The minimum and maximum absolute

errors in airflow rate predictions were 10.3 cfm

(0.00485 m3

/s) and 17,400 cfm (8.21 m3/s). The number

of exemplars within different relative error bins shows

that the number of exemplars with +/−3% error is 30, or

42% of all data (Fig. 14). For 85% of the exemplars, the

error is less than +/−8%. The average absolute value of

relative error of prediction for the whole set of exemplars

is 4.39%. These results suggest that the network in

Fig. 13 is capable of making reasonably accurate

predictions for a new data set. This network also looks

very promising for predicting ventilation airflow rates for

development mining.

In testing of the ANN in the presence of shielding

boreholes (Fig. 12B), the same procedure was applied.

ANN learning was tested against the training data set.

For this process, a training data set with 288 exemplars

was introduced to the network as the testing, or “new,”

data set. This analysis showed that the predicted values

were close to the target values of the data set. The

nominal MSE and R values were 0.0076 and 0.9962,

respectively. The low error and high correlation

coefficient are good indicators of predictive performance. The minimum and maximum absolute errors in

airflow rates for the predictions were 11.4 cfm

(0.00537 m3

/s) and 10920 cfm (5.15 m3/s), respectively.

In this phase, the relative errors using the predicted and

target airflow rates showed that the error fluctuated

within a narrow band. For most of the exemplars, the

error was +/−10%. The average of the absolute values of

the relative errors was 7.0%. The distribution of

exemplars within different error bins shows that 137

(48%) of all exemplars had a relative error +/−5%. The

number of exemplars with less than +/−10% relative

error was 222 (77%). For a +/−15% error range, the

number of exemplars within this range of error was 258,

or 89% of all data.

In the second stage, the performance of this ANN

(Fig. 12B) was assessed with a new data set containing

288 exemplars (testing set). The result indicated

reasonably good predictive capability for the new data

set and show that the network can recognize and

generalize patterns of input data. The minimum and

maximum absolute errors between predictions and targets

are 4.4 cfm (0.002 m3

/s) and 10,660 cfm (5.03 m3/s),

respectively with an NMSE of 0.00117 and an R value of

0.9942 (Fig. 15). The error histogram in Fig. 16 shows

that 85 of the exemplars (30% of the entire set) and 133 of

the exemplars (46% of the set) have less than +/−3 and

+/−5% error, respectively. This data also shows 208

exemplars (72%) with +/−10% error and 27 exemplars

(9%) with an error of +/−15% or more.

The comparison of ANN predictions for the airflow

rates and the error analyses show that the proposed

ANNs perform reasonably well for the prediction and

optimization of ventilation air requirements. The flexibility and predictive capability of trained ANNs offer

advantages in decision making and design processes.

5.4. Comparison of airflow rate predictions of the

trained ANN with the in-mine measurements

The trained ANN without shielding boreholes

predicted ventilation airflow rates during development

of tailgate and headgate entries shown in Fig. 1. Fig. 17

shows the measured airflow rates, reported as monthly

averages, and the predicted airflow rates using the

trained ANN shown in Fig. 12B. The following input

data (Tables 3 and 4) was collected and used by the

ANN to predict airflow rates: monthly average methane

concentration data measured in the mine, calculated

mining advance rates using reported monthly linear

mining distances, calculated average mining height

using reported monthly coal production tonnages and

the dimensions of the entries, and the calculated entry

length based on the monthly linear footage mined. As

shown in Fig. 17, the predictions of the trained ANN are

generally in good agreement with the field data. There

are a few data points that show relatively high error

compared to others, which may have arisen from the

calculated parameters that may have been different from

their actual field values.

Unfortunately, the predictions of the ANN with

shielding boreholes could not be compared with in-mine

measurements because of the absence of reported data.

However, it would be reasonable to expect acceptable

agreement with predictions and field data. One of the

advantages of developing ANNs in conjunction with

realistic simulations is that it increases the predictive

C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239 237
versatility of the ANN when other data sources are

limited.

Although the reservoir and the ANN models for

development mining were based on Pittsburgh Coalbed

parameters, the values of the coalbed reservoir properties may be varied in the parametric reservoir simulations. Developing and training potentially larger ANN

models would enable the mining and the coalbed

parameters to be generalized for operations in other

coal regions.

6. Summary and conclusions

This study presented the development and application of “dynamic” reservoir models and artificial neural

networks to predict and optimize methane inflows and

ventilation airflow requirements for development mining of coal seams. The reservoir models were developed

for a typical three-entry system practiced in the Northern

Appalachian Basin section of the Pittsburgh Coalbed.

These models considered the presence and absence of

boreholes against methane inflow. Model simulations

were performed by changing various mining and

degasification parameters in a systematic manner to

predict methane inflows and airflow requirements. The

reservoir model predictions were compared with the inmine measurements available from the mining of

tailgate and headgate entries around a longwall panel

in the Pittsburgh Coalbed.

The predictions from the reservoir simulations were

used to develop, train, and test the artificial neural

networks (ANNs). Various network structures were

tested to evaluate the predictive capabilities of the ANN.

The ANN predictions were compared with the reservoir

simulation results and the resulting prediction errors

were evaluated. Finally, the ANN was asked to predict

the airflow rates measured in the entries of the

Pittsburgh Coalbed mine using reported and calculated

mining parameters.

This study made the following conclusions:

1. Reservoir simulation can be used effectively for

modeling development mining in underground coal

mines. It can predict methane inflows into the entries

based on various coalbed and operating parameters,

from which the ventilation airflow requirements can

be predicted to maintain a desired methane level.

2. Methane inflow rate increases with entry length

because of increases in the surface area of the

coalbed exposed to the mine environment. These

increases are a logarithmic function of the mining

rate. However, the effect of the mining rate is less

pronounced on methane inflow for shorter development distances compared to longer development

distances.

3. For shorter development distances, marginal adjustments in ventilation airflow may be adequate to keep

methane levels under 1%. The model showed that

this adjustment was generally independent of the

mining rate. However, longer developments require

significantly more ventilation air capacity to keep

methane levels constant. This amount is a function of

the mining rate.

4. Employing shielding boreholes to protect entries

from methane migration during mining is an effective

approach. Even if the boreholes cannot be operated

for a long time prior to the start of mining, their

presence during mining makes a big difference (about

25%) in methane inflow rates, especially during

mining of longer sections.

5. Positioning of boreholes relative to entries is important. These results show that positioning wellbores as

close to the entries as practically possible is more

effective, especially when mining long developments

at higher mining rates.

6. Parametric and realistic reservoir simulations can

develop, train, and test ANN-based prediction and

optimization models, where actual field data is

unavailable. The ANNs developed in this study can

generate the reservoir simulation data with a

reasonable accuracy for predicting ventilation air

requirements during development mining.

7. ANN predictions of in-mine measured airflow data

were successful when using calculated monthlyaveraged input parameters for predictions rather than

the spatio-temporal data.

8. The ANN can be generalized to other coalbeds by

using parametric reservoir simulations to define the

impacts of different coalbed parameters on airflow

requirements and methane inflows.

References

Brunner, D.J., Schwoebel, J.J., Li, J., 1997. Simulation based

degasification system design for the Shihao Mine of the Songzao

coal mining administration in Sichuan China. Proc. 6th International Mine Ventilation Congress, Pittsburgh, PA, pp. 429–433.

Computer Modeling Group Ltd., 2003. Generalized Equation of State

Model-GEM. Calgary, Alberta, Canada.

Diamond, W.P., 1994. Methane control for underground coal mines.

Information Circular No. 9395, US Dept. of Interior, US Bureau of

Mines.

Diamond, W.P., La Scola, J.C., Hyman, D.M., 1986. Results of directmethod determination of the gas content of the U.S. coalbeds.

Information Circular No. 9067, US Dept. of Interior, US Bureau of

Mines.

238 C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239
Eberhart, R.C., Dobbins, R.W., 1990. Neural Network PC Tools: A

Practical Guide. Academic Press Inc., San Diego, CA.

Ertekin, T., Sung, W., Schwerer, F.C., 1988. Production performance

analysis of horizontal drainage wells for the degasification of coal

seams. Journal of Petroleum Technology 625–631.

Gorucu, F.B., Ertekin, T., Bromhal, G., Smith, D., Sams, N., Jikich, S.,

2005. Development of a neuro-simulation tool for coalbed

methane recovery and CO2 sequestration. 2005 International

Coalbed Methane Symposium, Tuscaloosa, AL. Paper 0505.

Hagan, M.T., Demuth, H.B., Beale, M.H., 1997. Neural Network

Design. PWS Publishing, Boston, MA.

Hardcastle, S.G., Dayss, A., Leung, E., 1997. Integrated mine

ventilation management system. Proceedings of 6th International

Mine Ventilation Congress. Pittsburgh, PA.

Karacan, C.Ö., Diamond, W.P., Esterhuizen, G.S., Schatzel, S.J., 2005.

Numerical analysis of the impact of longwall panel width on

methane emissions and performance of gob gas ventholes. 2005

International Coalbed Methane Symposium. Tuscaloosa, AL.

Paper 0505.

Karacan, C.Ö., Esterhuizen, G.S., Schatzel, S.J., Diamond, W.P., (in

press). Reservoir simulation-based modeling for characterizing

longwall methane emissions and gob gas venthole production.

International Journal of Coal Geology.

King, G., Ertekin, T., 1991. State of the art modeling for unconventional

gas recovery. SPE Formation Evaluation 63–72 (March).

Kosko, B., 1992. Neural Networks and Fuzzy Systems: A Dynamical

Systems Approach to Machine Intelligence. Prentice-Hall, Inc.,

Englewood Cliffs, NJ.

Mohaghegh, S., 2000. Virtual-intelligence applications in petroleum

engineering: Part 1-Artificial neural networks. Journal of Petroleum Technology 52 (9), 64–73.

NeuroDimension, 2006. NeuroSolutions 5.0.Gainesville. FL.

Noack, K., 1998. Control of gas emissions in underground coal mines.

International Journal of Coal Geology 35, 57–82.

Schalkoff, R.J., 1997. Artificial Neural Networks. McGraw-Hill, New

York.

Statsoft., 2003. Neural Networks. http://www.statsoftinc.com/textbook/

stneunet.html.

Thakur, P.C., Davis, J.G., 1977. How to plan for methane control in

underground coal mines. Mining Engineering 41–45 (October).

Thakur, P.C., Poundstone, W.N., 1980. Horizontal drilling technology

for advance degasification. Mining Engineering 676–680 (June).

Thakur, P.C., 1997. Methane drainage from gassy mines—a global

review. Proc. 6th International Mine Ventilation Congress.

Pittsburgh, PA, pp. 415–422.

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

1993. A simulation-based analysis of multiseam coalbed well

completions. 68th Society of Petroleum Engineers Annual

Technical Conference and Exhibition. Paper No 26628.

Zuber, M.D., 1997. Application of coalbed methane reservoir

simulators for estimation of methane emissions in longwall

mining. 6th International Mine Ventilation Congress. Pittsburgh,

PA, pp. 436–440.

Zuber, M.D., 1998. Production characteristics and reservoir analysis of

coalbed methane reservoirs. International Journal of Coal Geology

38, 27–45.

C.Ö. Karacan / International Journal of Coal Geology 72 (2007) 221–239 239