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International Journal of Coal G

Evolution of methane sorption capacity of coal seams as a function

of burial history—a case study from the Campine

Basin, NE Belgium

A. Hildenbrand a,*, B.M. Krooss b, A. Busch b, R. Gaschnitz c

a Flemish Institute for Technological Research (Vito), Boeretang 200, B-2400 Mol, Belgiumb Lehrstuhl fur Geologie, Geochemie und Lagerstatten des Erdols und der Kohle, Rheinisch-Westfalische

Technische Hochschule (RWTH) Aachen, Lochnerstr. 4-20, D-52056-Aachen, Germanyc aix-o-therm GeoEnergien, Beginenstrasse 5, 52062 Aachen, Germany

Received 8 February 2005; received in revised form 22 July 2005; accepted 27 July 2005

Available online 19 September 2005

Abstract

Based on extensive data sets of high-pressure sorption isotherms and canister desorption data from two Central European

coal basins (Campine and Ruhr basins) a computational scheme has been developed to calculate the maximum coal bed

methane (CBM) sorption capacity of coal seams as a function of pressure, temperature and coal rank. In addition, the effects of

in situ moisture content and maceral composition have been tentatively implemented. Using this algorithm it is possible to

explicitly take into account variations in sorption capacity of the coal seams in sedimentary basins over geologic time as a

function of burial history and thermal evolution.

The procedure has been applied to model the evolution and the present-day coal bed methane content of the Campine

Basin, NE Belgium. It is demonstrated how the present-day gas content of the Campine Basin is controlled by the burial

history of the coal layers throughout geologic time. The maximum gas sorption capacity typically occurs at a depth range

between 500 and 1000 m. During periods of uplift and erosion (~250 and 90 Ma before present) the uppermost coal layers

have lost methane due to a reduction of gas storage capacity while their storage capacity has increased during periods of burial

(~300 and 180 Ma ago, and present). Additionally, the present-day gas content is controlled by the gas generation history. Coal

layers, which have lost storage capacity during geologic time, will stay undersaturated if not replenished by late-stage (e.g.

microbial) gas.

The gas content profile of test well KB206 in the NE Campine Basin, established from canister desorption tests, can be

reproduced by assuming that undersaturation of the coals is due to erosion and re-burial, and that no significant gas generation

(e.g. microbial gas) has taken place after the time of maximal burial. Isotopic data (dD~178x, d13C~59x) indicate the

presence of a small portion of microbial gas. The absolute gas contents in this well are lower than the calculated maximum

0166-5162/$ - s

doi:10.1016/j.co

* Correspondi

E-mail addre

eology 66 (2006) 179–203

ee front matter D 2005 Elsevier B.V. All rights reserved.

al.2005.07.006

ng author. Tel.: +32 14 335909; fax: +32 14 321185.

ss: [email protected] (A. Hildenbrand).

A. Hildenbrand et al. / International Journal of Coal Geology 66 (2006) 179–203180

present-day sorption capacity. This may be due to an underestimation of the effect of water content on sorption capacity or result

from degassing via nearby faults, enforced by fluid circulation.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Gas adsorption; CH4; Coal; Burial history; Campine Basin

1. Introduction

Coal-bearing sequences in sedimentary basins

contain substantial amounts of natural gas generated

during the thermal decomposition (maturation) of

organic matter. Most of this gas is physically sorbed

in the microporous structure of the coal. Smaller

amounts may occur in the free gas phase present in

the macro- and mesopores and cleats and, to an

even lesser extent, as dissolved gas in the formation

water (e.g. Boyer and Qingzhao, 1998; Noak,

1998).

Fig. 1. Overview showing the location of the Campine

The amount of gas that can be physically sorbed by

a certain mass of coal is usually represented as a

function of gas (partial) pressure in terms of sorption

isotherms. Apart from pressure, the sorption capacity

of coals is determined by temperature, rank, maceral

composition and moisture content.

During recent years an increasing number of high-

pressure adsorption isotherms have been determined

experimentally for various coals of different rank and

at different temperatures. These provide the maxi-

mum gas sorption capacities of coals under well

constrained conditions and thus, key parameters for

Basin and test well Peer KB206 (NE Belgium).

Table 1

Langmuir parameters (KL and ml) for data published by Arets et al.

(1962); dry sample material; VM = volatile matter content,

R0=vitrinite reflectance, T = temperature

Sample VM

[%]

R0 [%],

calc.

T

[8C]KL

[MPa]

ml [Std.

m3/t, daf]

NL 5 3.20 25 0.473 27.70

NL 15 1.90 25 0.542 21.19

NL 25 1.30 25 0.693 17.29

NL 35 0.90 25 0.657 17.93

NL 40 0.75 25 0.835 20.68

NL 5 3.20 35 0.589 27.68

NL 15 1.90 35 0.627 20.31

NL 25 1.30 35 0.760 15.86

NL 35 0.90 35 0.915 17.58

NL 40 0.75 35 0.971 19.56

NL 5 3.20 50 0.781 26.20

NL 15 1.90 50 0.852 19.71

NL 25 1.30 50 0.970 15.02

NL 35 0.90 50 1.165 16.49

NL 40 0.75 50 1.058 17.38

Table 2

Langmuir parameters (KL and ml) for data published by Coppens

(1967); dry sample material; VM = volatile matter content, R0 =

vitrinite reflectance, T = temperature

Sample Ash

[%]

VM

[%]

R0 [%],

calc.

T

[8C]KL

[MPa]

ml [Std.

m3/t, daf]

Campine

Basin

10.4 30.96 1.05 20 0.95 25.3

Borinage

Basin

1.9 20.05 1.56 20 1.20 26.8

Borinage

Basin

2.0 18.55 1.65 20 0.49 27.9

Liege 3.5 7.36 2.74 20 0.94 33.7

A. Hildenbrand et al. / International Journal of Coal Geology 66 (2006) 179–203 181

the prediction of gas contents of coal seams in

sedimentary basins.

The prevailing gas saturation (gas-in-place) with

respect to maximum sorption capacity is, however,

controlled by numerous additional factors. These

comprise the amount of thermogenic gas that has

been generated, the amount of gas that was lost

from the system during uplift and erosion, the

hydrogeological regime and the amount of subse-

quent secondary biogenic (microbial) methane (Faiz

et al., 2003; Bachu and Michael, 2003). The latter

is considered of relevance for the gas saturation in

several basins: in the Southern Sydney Basin,

Australia, the Alberta Basin, Canada; or the Seely-

ville Coal Member, Indiana, USA (Faiz et al.,

2003; Bachu and Michael, 2003; Drobniak et al.,

2004).

In order to estimate the present-day gas content of

coal seams in the Campine Basin (NE Belgium) and

adjacent coal deposits, a review of adsorption data

determined on coal samples has been performed.

These experimental adsorption data are discussed

with respect to their consistency. Subsequently, the

change of adsorption capacity with increasing depth

and burial history is discussed. Finally, the present-

day coal bed methane content is calculated for test

well Peer KB206 located in the Campine Basin, Bel-

gium (Fig. 1).

2. Experimental sorption data used in the present

study

Tables 1–7 list the results of adsorption/desorption

experiments performed by various authors on coals

from the Campine Basin, Belgium, and the adjacent

Ruhr Basin, Germany. Background information on the

individual data sets and the experimental conditions

under which they were obtained is briefly outlined in

the following sections.

2.1. Campine Basin

Arets et al. (1962) performed adsorption experi-

ments on dry coals from coal mines in Limburg (NL)

up to 100 atm (10.13 MPa). Results of several low

pressure experiments (max. 400 mm Hg-column

~5.3 d 10�2 MPa) have been extrapolated to higher

pressures of up to 4 MPa. The measurements were

performed at 25, 35 and 50 8C. The Langmuir para-

meters in Table 1 are based on the figures published

by these authors.

Coppens (1967) conducted adsorption measure-

ments at 20 8C on four different coal samples

from Belgium (Table 2). Maximum pressures

achieved during the experiments were 10.2, 19.4,

38.6 and 19.2 MPa for one sample from the Cam-

pine Basin, two samples from the Borinage Basin

and one coal sample from Liege, respectively. Sam-

ples were measured in the dry state (max. moisture

content=0.25 wt.%).

Raven Ridge Resources Inc. (RRRI) carried out

adsorption tests on nine coal samples from the CBM

test well Peer KB206 (Table 3). The experiments

were performed under reservoir conditions, i.e. with

Table 3

Parameters for adsorption isotherms carried out on samples from well Peer KB206 by Raven Ridge Resources Inc. (RRRI); dry sample material;

ash = ash content, R0=vitrinite reflectance, KL and ml = Langmuir parameters

Sample WC [%] Ash [%] T [8C] Depth

[m]

R0

(meas.) [%]

R0

(calc.) [%]

Polynomial (valid for p b15 MPa): Langmuir parameter:

a b c d KL [MPa] ml [Std.

m3t/t, daf]

KS43 0.95 8.2 35 865 0.85 0.92 2.2E�2 �2.7E�1 1.90 0.11 1.60* 6.0*

KS44 3.56 2.8 36 907 0.97 1.3E�2 �2.9E�1 2.46 4.84 13.2

KS48a 2.38 1.2 39 978 0.86 2.7E�2 �6.2E�1 4.93 3.01 20.3

KS49 3.81 1.7 40 1001 0.87 8.1E�3 �2.0E�1 2.21 10.2 21.4

KS60 2.54 4.9 45 1168 1.00 0.94 9.6E�3 �1.9E�1 1.91 4.31* 11.0*

KS61 2.00 1.7 46 1185 1.01 0.98 2.0E�2 �4.1E�1 3.38 3.97* 16.6*

KS68 1.71 1.6 47 1218 1.04 8.3E�3 �2.3E�1 2.36 4.60* 13.6*

KS70 1.69 7.7 47 1238 1.04 1.07 1.0E�2 �2.2E�1 2.32 7.50* 18.2*

KS72 1.38 1.9 49 1287 1.07 6.2E�3 �1.8E�1 1.99 5.34* 12.5*

mads (Std. m3/t daf)=a *p3 �b *p2 +c *p +d, where p is the pressure in MPa.

* Less suitable fit to the measured data.

Table 5

Results from canister desorption tests carried out by DMT; ash =ash

content, R0=vitrinite reflectance

Sample

no.

Depth

[m]

Ash [%] VM

(meas.)

[%]

R0

(calc.)

[%]

ml [Std.

m3t/t, daf]

44, KS 50 1042.5 11.8 36.0 0.87 7.92

– 1043.5 3.3 36.0 0.87 8.54


samples containing their natural moisture content

(0.95–3.81 wt.%), and at temperatures ranging

between 35 and 49 8C. Only three isotherms could

be fitted by the Langmuir model. The isotherms of

the other six samples were fitted by polynomial

functions. Additionally, the gas contents of the

coals were determined by canister desorption tests

(RRRI, 1993; Table 4).

Deutsche Montan Technologie (DMT, 1993) car-

ried out 20 canister desorption tests on samples from

well Peer KB206 (Table 5).

2.2. Ruhr Basin

Table 6 lists data of sixteen adsorption experiments

by Denneberg (1998) on dry coal at 125 and 50 8C

Table 4

Results from canister desorption tests (RRRI); ash =ash content,

R0=vitrinite reflectance

Sample

no.

Depth

[m]

Ash

[%]

VM

(meas.)

[%]

R0

(meas.)

[%]

R0

(calc.)

ml [Std.

m3t/t, daf]

KS39 855.0 8.2 36.9 0.84 2.34

KS44 906.5 2.8 34.8 0.91 4.68

KS48a 978.3 1.2 36.3 0.86 5.87

KS49 1001.3 1.7 36.4 0.86 6.47

KS60 1168.3 1.9 29.8 1.00 1.10 8.60

KS61 1184.9 1.7 32.4 1.01 0.99 7.89

KS68 1217.8 1.6 60.6 0.26 6.21

KS70 1238.3 7.7 31.0 1.04 1.05 6.87

KS72 1286.9 1.9 28.7 1.14 6.39

and up to 18.5 MPa. The experiments were conducted

with distinct macrolithotypes: 3 vitrain and 13 clarain

samples.

Gaschnitz (2000) conducted forty-five high-pres-

sure CH4-adsorption experiments up to 20 MPa, at

temperatures from 37 to 175 8C on dry coals and

49, KS 50 1063.3 3.7 35.7 0.88 7.45

55 1093.1 9.5 34.0 0.94 7.84

58 1103.4 4.0 32.7 0.98 8.16

58 1103.9 13.9 32.7 0.98 7.87

59, KS54 1109.2 47.0 32.7 0.98 7.66

62 1121.9 43.7 32.7 0.98 6.91

72 1155.7 6.0 34.3 0.93 8.22

75 1167.0 4.7 34.3 0.93 7.26

77, KS60 1174.2 25.1 34.0 0.94 6.65

– 1175.9 3.8 34.0 0.94 6.19

79, KS61 1185.5 10.1 33.0 0.97 7.14

– 1166.6 7.8 31.9 1.01 7.75

87, KS66 1218.8 1.7 32.0 1.01 7.83

– 1218.6 7.3 32.0 1.01 7.48

91 1238.6 13.7 33.0 0.97 7.02

92 1239.9 3.7 34.1 0.93 6.63

97 1259.1 1.9 34.1 0.94 7.45

112 1320.7 3.8 34.0 0.94 2.79

Table 6

Langmuir parameters (KL and ml) for selected data published by Denneberg (1998); dry sample material; V = vitrinite content, L = liptinite

content, I = inertinite content, ash = ash content, VM, calc. = volatile matter content (calculated from R0), R0 meas. = measured vitrinite

reflectance, T = temperature

Sample Lithotype V [%] L [%] I [%] Ash [%] VM (calc.)

[%]

R0 (meas.)

[%]

T [8C] KL [MPa] ml [Std.

m3/t, daf]

970196 Clarain 90 4 6 3 36.3 0.86 125 6.82 14.8

970198 Vitrain 100 0 0 2 35.7 0.88 125 11.09 23.3

970201 Clarain 71 10 19 1 34.8 0.91 125 5.50 13.1

970202 Clarain 91 4 5 4 34.5 0.92 125 7.38 16.7

970207 Clarain 91 5 4 9 34.8 0.91 125 7.15 16.5

970209 Clarain 72 9 19 29 34.0 0.94 125 13.77 16.3

970218 Clarain 83 8 9 7 32.8 0.98 125 4.67 12.0

970223 Clarain 88 6 6 4 31.7 1.02 125 6.77 15.8

970224 Clarain 54 8 38 3 30.4 1.07 125 5.71 14.5

970231 Clarain 73 9 18 35 30.7 1.06 125 5.55 12.2

970239 Clarain 85 8 8 10 29.6 1.10 125 11.88 16.6

970240 Vitrain 97 2 1 1 29.6 1.10 125 9.48 21.0

970312 Vitrain 100 0 0 1 34.3 0.93 125 5.92 17.7

970223 Clarain 88 6 6 4 31.7 1.02 50 3.24 23.8

970231 Clarain 73 9 18 35 30.7 1.06 50 4.60 23.7

970223 Clarain 88 6 6 4 31.7 1.02 50 1.76 20.0


samples with moisture contents up to 12.8%. Samples

used for the measurements represent distinct macro-

lithotypes, separated into vitrain, fusain, and saprope-

lic coal (Table 7).

3. Parameterisation and comparison of adsorption

data

The experimental methane sorption data from the

different sources listed above have been parame-

terised in terms of the Langmuir sorption isotherm

function:

mads ¼ mldp

KL þ pð Þ : ð1Þ

Here, KL [MPa] denotes the Langmuir pressure,mads is

the mass of adsorbed gas at a distinct pressure ( p

[MPa]), and ml is the maximum amount of adsorbed

gas at complete surface coverage (Table 1). The para-

meters mads and ml are reported in Std. m3/t dry ash-

free coal.

Although the Langmuir model is strictly valid

only for monolayer adsorption at low pressures, it

has been found to be applicable to methane

adsorption at intermediate and high pressures with

a sufficient degree of precision (e.g. Busch et al.,

2003).

The Langmuir parameters were determined indi-

vidually by best fit regressions. In Fig. 2 the Lang-

muir parameters of the different data sets are plotted

as a function of vitrinite reflectance. While the

entire data set contains 93 data points with a max-

imum temperature of 125 8C, Fig. 2 is based on a

reduced data set for measurements between 20 and

50 8C (n =33). For the data of Arets et al. (1962)

ash contents were not reported and a conversion to

the daf-basis was thus not possible. According to

the VITO database (Gekko) containing information

on all coal exploration wells drilled in the Campine

Basin, the average ash content of coals in the

Campine Basin is 8.6 wt.% (std. dev.=F8.9%,

n=9768). A conversion of the data from Arets

et al. (1962) to a daf-basis would lead to an approx-

imate increase of the ml values by about 9% (error

bars in Fig. 2). Most data points in Fig. 2 are

derived from measurements on dry material. Excep-

tions are the data sets from Coppens (1967)

(WCb0.25 wt.%) and those for the Peer KB206

well (WC=2.4–3.8 wt.%).

The highest monolayer sorption capacities (ml)

are observed for the sapropelic coals and vitrain sam-

Table 7

Langmuir parameters (KL and ml) for some selected data published by Gaschnitz (2000); dry sample material; VM, calc. = volatile matter

content (calculated from R0), R0 meas. = measured vitrinite reflectance, T = temperature, WC = water content

Sample Lithotype VM, calc.

[%]

R0 meas.

[%]

T [8C] WC [wt.%] KL [MPa] ml [Std.

m3/t, daf]

43108 Fusain 20 1.55 37 dry 5.71 18.1

43108 Fusain 20 1.55 75 dry 6.53 14.5

43108 Fusain 20 1.55 100 dry 6.98 13.8

43108 Fusain 20 1.55 125 dry 8.67 13.4

43108 Vitrain 20 1.55 105 dry 3.24 22.8

43108 Vitrain 20 1.55 90 dry 1.84 23.1

43108 Vitrain 20 1.55 90 dry 2.49 23.6

43108 Vitrain 20 1.55 75 dry 1.99 25.8

43108 Vitrain 20 1.55 60 dry 2.31 29.2

43108 Vitrain 20 1.55 60 dry 2.90 31.1

43108 Vitrain 20 1.55 45 dry 2.74 32.9

43399 Vitrain 35 0.89 35 dry 4.10 32.8

43399 Vitrain 35 0.89 45 dry 3.08 26.4

43399 Vitrain 35 0.89 60 dry 3.66 24.5

43399 Vitrain 35 0.89 75 dry 3.40 21.9

43399 Vitrain 35 0.89 35 dry 3.21 30.8

43399 Vitrain 35 0.89 45 dry 4.33 28.0

43399 Vitrain 35 0.89 60 dry 2.94 21.6

43399 Vitrain 35 0.89 75 dry 3.40 21.9

43399 Vitrain 35 0.89 90 dry 5.79 22.5

43399 Vitrain 35 0.89 90 dry 5.53 22.5

43399 Vitrain 35 0.89 105 dry 6.17 21.3

43399 Vitrain 35 0.89 125 dry 11.07 24.0

43399 Vitrain 35 0.89 125 dry 6.93 17.8

43399 Vitrain 35 0.89 150 dry 9.42 18.7

43399 Vitrain 35 0.89 175 dry 9.75 15.4

43399 Vitrain 35 0.89 175 dry 9.48 14.9

43399 Vitrain 35 0.89 75 4.7 3.16 12.5

43399 vitrain 35 0.89 45 4.7 3.25 17.9

43399 Vitrain 35 0.89 60 4.7 4.10 16.3

43408 Sapropelic coal 34 0.93 50 dry 2.06 33.1










43408 Sapropelic coal 34 0.93 50 1.8 3.74 31.1

43408 Sapropelic coal 34 0.93 50 3.5 4.28 28.6

43408 Sapropelic coal 34 0.93 50 6.3 4.38 25.1

43408 Sapropelic coal 34 0.93 50 12.8 3.48 22.6

43408 Sapropelic coal 34 0.93 30 1.8 2.79 29.9

43408 Sapropelic coal 34 0.93 60 1.8 2.82 23.5

43408 Sapropelic coal 34 0.93 90 1.8 6.46 21.9

43408 Sapropelic coal 34 0.93 125 1.8 8.87 19.1

43408 Sapropelic coal 34 0.93 150 1.8 23.79 20.1


0

5

10

15

20

25

30

35

40

45

50

0 1 2 3 4

vitrinite reflectance, R0 [%]

m∞

[Std

. m3 /

t]

U-shaped trend(Arets et al., 1962)

linear trend for samples fromCoppens (1967),Denneberg (1998) andPeer KB206

0

2

4

6

8

10

12

0 2 31 4

vitrinite reflectance, R0 [%]

KL [

MP

a]

Arets (dry)CoppensGaschnitz, fusainGaschnitz, saprop.Gaschnitz, vitrainDenneberg, clarainPeer KB206

Fig. 2. Comparison of the Langmuir parameters derived from different data sets. With the exception of the data from Arets et al.

(1962) all data are reported on a dry and ash-free basis. The plot contains data from measurements at 20–50 8C and, if available,

data for dry material itself. Exceptions are the data sets from Coppens (1967) (WCb0.25%) and those for well Peer KB206

(WC=2.4–3.8 wt.%).


ples documented by Gaschnitz (2000). Lowest values

are found for the data set of Arets et al. (1962), the

fusain sample from Gaschnitz (2000) and one sample

from the test-well Peer KB206. The clarain samples

from Denneberg (1998), Coppens (1967) and two

samples from Peer KB206 plot in between and show

a linear increase of ml with increasing maturity. This

is in contrast to the data set of Arets et al. (1962)

suggesting that the ml values exhibit a minimum at

intermediate maturity levels (U-shaped ml vs. matur-

ity plot).

The lowest KL-values were obtained for the data

from Arets et al. (1962) and Coppens (1967). All

other Langmuir pressures were higher.

Differences in maximum sorption capacities may

be attributed to various reasons:

1) Extrapolations from low to high pressures (Arets

et al., 1962).

2) Different maceral compositions: There is an

obvious difference in adsorption capacity of dis-

tinct macrolithotypes. The high sorption capacities

documented by Gaschnitz (2000) on the vitrain

samples may be due to the high vitrinite contents

of these samples. In comparison, the data from

Coppens (1967) were derived from measurements

on bulk coal samples, containing different maceral

types. The clarain samples (Denneberg, 1998)

included in Fig. 2 have vitrinite contents between

73% and 89%.

3) Differences in moisture content: The low maxi-

mum sorption capacities for the test-well Peer

KB206 can be explained by the fact that measure-

ments were performed on moist material.

4) Temperature dependence: The relatively high sorp-

tion capacities reported by Coppens (1967) are due

to the low measuring temperature of 20 8C. Gen-erally, sorption capacities decrease with increasing

temperature. The measurements of Gaschnitz

(2000) and those on the coals from the Peer

KB206 well were performed in the temperature

range from 35 to 40 8C.

4. Parameters affecting the gas sorption capacity of

coals

It is known that the gas sorption capacity of coals is

a function of pressure, temperature, coal rank, maceral

composition and water content. While the pressure

and temperature dependence of the gas sorption capa-

city have been established in numerous studies (e.g.

Krooss et al., 2002), other relationships (e.g. depen-

dence on rank or maceral composition) are less evi-

dent or documented and quantified only in a very

general way (e.g. dependence on moisture content).


4.1. Effect of pressure

Generally, the sorption capacity increases with

increasing pressure until a certain saturation pres-

sure is reached. In the present study the Langmuir

model was used to represent this pressure depen-

dence and it was found to adequately describe most

of the experimental data sets analysed. The adsorp-

tion measurements performed by Raven Ridge

Resources Incorporated, however, showed unex-

pected features and could not be fitted by

the Langmuir model. Instead of asymptotically

approaching an adsorption limit (ml), these iso-

therms show a steep increase in adsorption capacity

at higher pressures (above approximately 10 MPa).

This observation is in contrast with many high-

pressure methane sorption isotherms published

since. It casts some doubt on the reliability of this

data set and might be due to leakage problems

during isotherm measurements.

4.2. Effect of temperature

It is known that adsorption is an exothermic

process and therefore adsorption capacity decreases

with increasing temperature (Sircar, 1992). The tem-

perature dependence of the sorption capacity is con-

trolled by the isosteric heat of sorption which, in

turn, depends on the surface coverage. For ideal

systems, thermodynamic analysis can provide infor-

mation on the temperature dependence of sorption

capacity (e.g. Krooss et al., 2002). Due to the com-

plexity of natural coals, however, a precise predic-

tion of this temperature dependence is usually

impossible. For the present investigation a linear

relationship between sorption capacity and tempera-

ture was assumed in the regression analysis as a first

approximation.

4.3. Effect of rank

Experimental sorption data by Arets et al. (1962)

indicate that the amount of adsorbed gas is highest

for mature samples and passes through a minimum

for coals with a volatile matter content of 27%

(~1.2% R0). The data from Coppens (1967) show a

linear increase in sorption capacity with increasing

coal rank. The highest adsorption capacity was deter-

mined for the sample from Liege with a volatile

matter content of 7.36% (vitrinite reflectance

~2.74% R0), the lowest capacity was found for the

sample from the Campine Basin with a volatile

matter content of 30.96% (~1.05% R0). The data of

Gaschnitz (2000) also show increased adsorption

capacities for higher maturities. No distinct trend

with coal rank could be established from the data

of Denneberg (1998) covering the range from 0.86%

to 1.1% R0 (Table 6).

The dependence of CH4 sorption capacities on

rank and pore structure of coals has been discussed

for various other data sets e.g. by Nodzenski (1998),

Laxminarayana and Crosdale (1999, 2002), Levy et

al. (1997) and Gluskoter et al. (2002). Levy et al.

(1997) found a clear positive relation between CH4

sorption capacities of moist coals with carbon con-

tent (rank). In contrast, the results for dry samples

did not show a clear trend; the data were rather

scattered and showed a sharp increase of sorption

capacities above a carbon content of about 90%. The

behaviour for the moist coals found by Levy et al.

(1997) is confirmed by Laxminarayana and Crosdale

(2002) who reported a linear increase of CH4 sorp-

tion capacities with rank for high- to low volatile

moist Indian coals. In contrast, dry coals showed a

decrease in maximum CH4 sorption capacities with

rank. In an earlier study on Australian coals, Laxmi-

narayana and Crosdale (1999) found that CH4 sorp-

tion capacities on dry coals displayed a bUQ-shapedtrend with rank with a minimum between 1.5% and

2.0% R0 max. Prinz (2004, 2005) demonstrated for a

suite of nine Pennsylvanian coals from the Ruhr

Basin, analysed at 40 8C, that dry coals exhibit a

bUQ-shaped trend, as described by Arets et al. (1962)

and Laxminarayana and Crosdale (1999). However,

the same samples investigated in the moist state

show a linear increase in sorption capacity from

high-volatile bituminous to semi-anthracite rank

(Prinz, 2004, 2005).

The initial decrease in CH4 capacities with rank is

attributed to the blocking of micropores by higher

molecular-weight hydrocarbons. During increasing

coalification, the micropores are believed to open

up due to cracking of the hydrocarbon components

so that additional sorption capacity becomes avail-

able (e.g. Gan et al., 1972). This behaviour is

assumed to occur predominantly in vitrinites due to


the higher abundance of micropores as compared to

inertinites.

4.4. Effect of maceral composition

Denneberg (1998) found that methane sorption

capacity increases with increasing vitrinite content

and decreasing liptinite content. The results of

Gaschnitz (2000) reveal differences between distinct

lithotypes. In contrast to the data set of Denneberg

(1998), a sapropelic coal, which contains higher

amounts of liptinite, had a higher sorption capacity

than a vitrain sample of similar maturity. The lowest

sorption capacity was measured for a fusain sample

(1.55% R0).

Many studies on CH4 adsorption have investigated

the effects of maceral composition of coals of various

rank and locations (e.g. Lamberson and Bustin, 1993;

Crosdale et al., 1998; Levy et al., 1997; Laxminar-

ayana and Crosdale, 1999, 2002; Bustin and Clarkson,

1998; Ettinger et al., 1966; Ryan and Lane, 2002;

Mastalerz et al., 2004). Studies on Australian and

Canadian coals showed that maceral composition is

an important control on CH4 adsorption but the degree

of influence is rank-dependent (Lamberson and Bus-

tin, 1993; Crosdale et al., 1998; Bustin and Clarkson,

1998; Laxminarayana and Crosdale, 1999). Most of

these studies concluded that vitrinite is the most

important maceral favouring CH4 sorption on coal

compared to similar-rank inertinite. In contrast, inves-

tigations on Indian coals showed that the vitrinite

content does not influence CH4 sorption capacities

(Laxminarayana and Crosdale, 2002). Bustin and

Clarkson (1998) demonstrated for coals from the

USA, Canada and Australia that sorption capacities

decrease with increasing inertinite and ash contents

and increase with increasing vitrinite contents for iso-

rank coals. This might be due to higher specific sur-

face areas related to higher micropore capacities in

vitrinites (e.g. Crosdale et al., 1998; Unsworth et al.,

1989). In contrast to other authors, Ettinger et al.

(1966) documented for hand-picked macerals from

the Donetsk Basin, Ukraine, that inertinite exhibits

higher sorption capacities for CH4 than vitrinite.

Results by Busch et al. (2003) indicate that rank rather

than maceral composition influences CH4 sorption

capacity. Mastalerz et al. (2004) demonstrated that

the CO2 sorption volumes are positively correlated

with the content of maceral telocollinite; no trend

with maceral composition could be established for

the CH4 sorption capacity. Apart from the study of

Denneberg (1998) no information on the influence of

liptinite on the CH4 sorption capacity was found in the

literature.

4.5. Effect of water content

The gas sorption capacity of coals is strongly

affected by the presence of water. Depending on the

water content, the sorption capacity can apparently be

reduced by 60% to 90% as compared to the dry state.

Generally, sorption capacity decreases continuously

with increasing water content until a certain critical

level is reached. Above this critical water content it

remains nearly constant (Gaschnitz, 2000; Joubert et

al., 1973, 1974).

A common reference state is the equilibrium

water content established at 30 8C and 96–97%

relative humidity. For a sapropelic coal with a vitri-

nite reflectance of 0.93% and an equilibrium water

content of 3 to 4.5 wt.%, a moisture content of only

1.8 wt.% was found to reduce the sorption capacity

by 66–84% (Gaschnitz, 2000). Upon further increase

of the moisture content up to 6 wt.% the methane

sorption capacity was reduced by 51–63%, with

respect to the sorption capacity in the dry state.

Temperature and pressures above 7.5 MPa do not

have a significant influence on the mads(dry) /mads

(moist)-ratio.

Some authors propose a linear relationship between

moisture content and the reduction of sorption capacity

with factors of 0.23 (Joubert et al., 1973), 0.31 (Ettinger

et al., 1958) and 0.39 m3/(td% H2O) (Killingley et al.,

1995). Non-linear correlations between moisture con-

tent and adsorption capacity were established empiri-

cally by Joubert et al. (1974) for a temperature of 30

8C and for pressures up to 6 MPa.

For water-undersaturated coals (below the critical

water content; after Joubert et al., 1974):

mads dryð Þmads moistð Þ ¼ 0:7461d pd1:45ð Þ�0:2009

dWCþ 1 ð2Þ

where p is the pressure [MPa] and WC is the water

content [wt.%].


For water-saturated samples (Joubert et al.,

1974):

mads dryð Þmads moistð Þ ¼ 1þ 5:0535d 10�5d pCH4

� 0:0576� �

d X0 � 10�7:221d 10�3d pCH4�1:0194: ð3Þ

Here X0 is the oxygen content [wt.%] of the coal.

The oxygen content of coals appears to affect the

sorption capacity in a similar way as the moisture

content (Prinz, 2004). Generally, the oxygen content

of coals decreases with increasing rank. This holds

also for the equilibrium moisture content (critical

water saturation). For the Ruhr Basin Gaschnitz

(2000) proposed the following correlation:

WCcrit ¼ 1:436þ O

6:114

� �3:001

;

R2 ¼ 0:883; n ¼ 448: ð4Þ

Here WCcrit denotes the critical water saturation and

O the oxygen content [wt.%].

In the Campine Basin coals the water content ranges

between 40% and 100% of the critical water content as

Water content of coal s

0

1

2

3

4

5

6

7

8

0.5 0.6 0.7 0.8 0.9 1.0 1.

vitrinite refec

wat

er C

on

ten

t, W

C [

%]

Fig. 3. Actual (WC) and critical water content (WCcrit) of coals from t

according to a formula suggested by Gaschnitz (2000) for the Ruhr Basin

determined using the formula of Gaschnitz (2000).

Both, the critical and prevailing water content (includ-

ing only the adsorbed water and the water held by

capillary forces) change as a function of coal rank

(Fig. 3):

WCcrit ¼ 2:35d R�2:370 ; R2 ¼ 0:52; n ¼ 90 ð5Þ

WC ¼ 1:61d R�2:250 ; R2 ¼ 0:52; n ¼ 203: ð6Þ

From 0.6% to 1.2% R0 the average measured water

content (WC) drops from approximately 6 to 1 wt.%.

For higher rank samples (R0N1%) the water content

does not change significantly (1–2 wt.%). Similar

findings are reported for the Ruhr Basin where the

coals are undersaturated in water by 50–65% with

respect to the critical water content (Gaschnitz, 2000).

5. Sorption capacity estimation algorithms

During burial in sedimentary basins, coal seams are

exposed to elevated pressures and temperatures which,

in combination with the moisture content, represent

primary controls on the gas storage capacity. Further-

amples Campine Basin

WCcrit = 2.35 * R0-2.35 (R2 = 0.52)

WC = 1.61 * R0-2.25 (R2 = 0.54)

1 1.2 1.3 1.4 1.5 1.6 1.7

tance R0 [%]

WC

WCcrit

he Campine Basin; the critical water content has been determined

.

Table 8

Overview of the different data sets and the parameters available for

multiple regression; the data of data sets 1–4 are given on a dry and

ash-free (daf) basis

Data set 1 n (Coppens) 4 (all data, daf)

n (Denneberg) 16 (all data, daf)

n (Gaschnitz) 25 (vitrinite-rich material,

fusain and sapropelic

coals excluded, daf)

Independent variables R0, T, (WC)

Here only a few

measurements are

available for different

water contents

(see data set (2))

Data set 2 n (Coppens) 4 (all data, daf)

n (Gaschnitz) 6 (measurements on

vitrain samples with

respect to WC)

Independent variables R0, T, WC

Data set 3 n (Denneberg) 16 (all data, daf)

Independent variables R0, T, V

Data set 4 n (Gaschnitz) 6 (sapropelic coals;

only performed for

one coal rank level, daf)

Independent variables T, WC

Data set 5 n (Arets) 15 (all data)

Independent variables R0 or VM, T


more, coals undergo chemical and physical transforma-

tions that also affect their gas storage capacity. One

prerequisite for a comprehensive analysis of the coal

bed methane inventory of a sedimentary basin is an

algorithm that takes into account the primary and sec-

ondary controls on the gas sorption capacity of coals.

It appears reasonable in this respect to differentiate

between the influence of (i) inherent properties of the

coal, such as coal type, maceral composition and ash

content, (ii) rank or maturity, and (iii) exogenic influ-

ences such as temperature, pressure and water content.

Ash content (which exhibits a relative increase with

increasing coal rank) is accounted for by normalisa-

tion to the dry and ash-free state of the coal.

Maceral composition is pre-determined by the

depositional conditions of the coal and therefore invar-

iant in the further evolution of the seam. But, as out-

lined above, information on the influence of maceral

composition on the sorption capacity of coals remains

ambiguous with a tendency to attribute higher sorption

capacity to vitrinites. Macerals of the coals in the

Campine and Ruhr Basin are dominated by vitrinite.

Relationships derived in this work will therefore hold

true primarily for this geographical region. However, a

term is provided in the algorithm to account for the

influence of vitrinite contents derived from the mea-

surements of Denneberg (1998). Future conceptual

models or alternative relationships can be readily

implemented as they become available.

Although its influence on the methane sorption

capacity of coals is still associated with considerable

uncertainties, rank (expressed in terms of vitrinite

reflectance or volatile matter content) represents a

primary parameter for the algorithm developed in

this study. The regression constants were derived

from experimental data on coals covering the maturity

range most relevant for the study area.

While the maceral composition of a coal seam is

time-invariant and coal rank evolves on a geologic

time scale according to burial and thermal history,

pressure, water content and temperature are first-

order control parameters determining the gas sorption

capacity of a coal seam at a given geological situation.

In the present study the pressure dependence of gas

sorption capacity is represented by the Langmuir

equation (see above).

Based on selected analytical data sets (Tables 8

and 9) a multiple regression approach was used to

relate the Langmuir parameters (ml and KL) to the

combined effects of the independent variables (R0, T,

WC, V). Details of this regression procedure and the

results of the individual regressions for the Langmuir

monolayer sorption capacity ml are summarised in

Table 10. The corresponding results for the Lang-

muir pressure KL are listed in Table 11.

5.1. Data sets

The adsorption data of Arets listed in Table 1

represent the Langmuir parameters for selected data

points from published graphs (Figs. 4–6 in Arets et al.,

1962), which account for extrapolations and interpo-

lations of the author. In comparison, the data of

Coppens (1967), Denneberg (1998) and Gaschnitz

(2000) represent individual sorption isotherm mea-

surements (Tables 2, 6, 7). Therefore the data of

Arets et al. (1962) were evaluated separately. Because

most of the reported adsorption test results for well

Peer KB206 did not follow the Langmuir trend (Table

3), these data have been excluded.

Table 9

Ranges of regression parameters of data sets 1–5; C = Coppens, D = Denneberg, G = Gaschnitz, A = Arets

Data set 1 2 3 4 5

Vitrinite-rich coals

(CDG)


(CG)


(D)

Sapropelic coals

(G)


(A)

R0 [%] 0.86–2.74 0.89–2.74 0.38–0.62 0.93 (const.) 0.75–3.2

VM [%] 7.36–36.3 7.36–35 16.2–22.9 34 (const.) 5–40

T [8C] 20–175 20–75 50–125 30–175 25–50

WC [%] 0–4.7 0–4.7 0 (const.) 0–12.8

V [%] 54–100

L [%] 0–10

I [%] 0–38


The data sets used for multiple regression are listed

in Table 8. The different data sets were collated with

respect to the availability of the pertaining parameters

maturity (R0, VM), temperature (T), water content

(WC) and vitrinite content (V). The data ranges of

the individual data sets are summarized in Table 9.

With the exception of the data set 5 published by

Arets et al. (1962), where no ash content data are

available, all data were calculated on a dry, ash-free

basis.

5.2. Langmuir parameter ml

The Langmuir monolayer sorption capacity ml

was expressed using the standard form of the multiple

linear regression:

Y ¼ aþ b Xið Þd Xi i ¼ 1; 2; . . . nð Þ: ð7Þ

Here the Xi denotes the independent variables

while a and b(Xi) are the coefficients that were

adjusted by a least-square fitting procedure. The

regression was performed simultaneously over two

or more parameters. The results of the regression

analyses and information on the quality of fits are

summarised in Table 10. The validity of the regres-

sions is determined by the data ranges of the corre-

sponding data sets (Table 9). Linear trends were

obtained for the parameters maturity (R0), temperature

(T), and vitrinite content (V) (Figs. 4 and 5):

ml R0; Tð Þ ¼ aþ b R0ð Þd R0 þ b Tð Þd T

þ b Vð Þd V data sets 1� 4ð Þ: ð8Þ

In order to match the ml-data of Arets et al. (1962,

data set 5, Table 8), the regression function was

extended by a square term for the maturity parameter

(VM, volatile matter content %, Fig. 6):

ml R0; Tð Þ ¼ aþ b1 VMð Þd VMþ b2 VMð Þd VM2

þ b Tð Þd T data set 5ð Þ: ð9Þ

The volatile matter contents (VM) can be con-

verted to vitrinite reflectance values according to

Rice (1993):

R0 ¼ � 2:712d log VMð Þ þ 5:092

15%bVMb40%ð Þ: ð10Þ

The effect of water content on the Langmuir mono-

layer sorption parameter is taken into account by a

normalized 3-parameter exponential decay function.

The critical water saturation is taken from Fig. 3 and

the other two parameters decay constant bkQ and decayrange brQ are determined by regression. Thus the

following equation results for the dependence of ml

on maturity, temperature and water content:

ml R0; T ;WCð Þ ¼ ml R0; Tð Þd rd exp � kd WC=WCcritð Þ½þ 1� rð Þ�: ð11Þ

This relationship implies that no substantial

decrease of the sorption capacity occurs beyond

the critical water saturation WCcrit (Fig. 7). Sys-

tematic measurements of sorption data as a function

of moisture content on individual coals are still very

limited. Regression analysis of the data measured by

Gaschnitz (2000) on sapropelic coals at 50 8C (data

set 4, n =6, WCcrit =4%) yielded a value of 0.6 for

the decay constant k and a value of 0.4 for the

coefficient r. The regressions for data sets 1 and 2

result in values of 5.6 and 1.2, respectively, for the

Table 10

Overview of multiple regression results for Langmuir parameter ml; WCcrit pre-defined and given as a function of maturity (see Fig. 2); C = Coppens, D = Denneberg, G =

Gaschnitz, A = Arets

Comment Symbols/

constants

1 (CDG vitrinite-rich

coals)

2 (CG vitrinite-rich coals) 3 (D vitrinite-rich coals) 4 (G sapropelic coals) 5 (A)

With

fixed

k =0.6

With

fixed

k =0.6

Const.

T=125 8CConst.

WC=0%

Const

T =50 8C(Fig. 6)

All

data

With

fixed

k =0.6

n (C) 4 4

n (D) 16 16 13

n (G) 25 6 10 6 19

n (A) 15Pn 45 10 16 13 10 6 19 15

Use formulas a 23.68 27.78 27.44 23.09 25.13 24.08 34.79 15.63 4.30 3.53 38.46 38.19 40.06 35.58

8 and 9 b(R0) 4.84 3.57 3.26 4.60 3.59 3.76 �7.77 �0.35 �0.68

b(T) �0.09 �0.11 �0.10 �0.12 �0.08 �0.06 �0.09 �0.08 �0.07 �0.07 �0.08 �0.08

b(L)

b(V) 0.14 0.15 0.16

b1(VM) �1.16

b2(VM) 0.02

Use formulas WCcrit 1.9 1.9 1.4 1.4 2.8 2.8 2.8

5 and 11 av.ml(WC0) 35.79*

r 0.36 0.46 0.34 0.39 0.40 0.45 0.31

k 5.24 0.60 1.61 0.60 0.60 0.60 2.96

Independent variables

used in multivariate

analyses

R0, T R0, T,

WC

R0, T,

WC

R0, T R0, T,

WC

R0, T,

WC

R0, T R0, T,

V

R0, V V T WC T,

WC

T,

WC

VM,

T

Number of variables

used

2 3 3 2 3 3 2 3 2 1 1 1 2 2 2

R 0.77 0.86 0.85 0.82 0.97 0.97 0.68 0.80 0.71 0.71 0.81 0.95 0.79 0.88 0.99

R2 0.59 0.74 0.72 0.67 0.94 0.94 0.46 0.64 0.50 0.50 0.66 0.90 0.62 0.77 0.99

Adjusted R2 0.57 0.72 0.70 0.66 0.94 0.99 0.38 0.55 0.42 0.46 0.61 0.88 0.58 0.75 0.98

* In order to obtain a value for the Langmuir parameter ml multiply formula 11 with the av.ml(WC0), which is the average ml at WC=0.

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Table 11

Overview of multiple regression results for Langmuir pressure KL; C = Coppens, D = Denneberg, G = Gaschnitz, A = Arets; (1) KL negative for R0N0.03 d T +1.41 (1, linear trend)

Comment Symbols/

constants

1 (CDG vitrinite-rich coals) 2 (CG vitrinite-rich coals) 3 (D vitrinite-rich coals) 4 (G sapropelic coals) 5 (A)

Validity

restricted (1)

Const.

T=125 8CConst.

WC=0%

Const.

T =50 8CAll data

n (C) 4 4

n (D) 16 16 13

n (G) 25 6 10 6 19

n (A) 15Pn 45 10 16 13 10 6 19 15

Linear regressions: a 26.57 11.46 11.08 �42.26 �25.10 �73.22 0.14 47.21 �9.11 �44.92 �44.93 0.71

formulas 12 and 15 b(R0) �18.96 �5.12 �4.71 24.52 43.16 41.1 0.37

b(VM) 0.10

b(log(R0)) �11.99

b(T) 0.53 0.43 0.41 0.64 0.64 0.78 0.95 0.13

b(log(T)) 42.02

b(V) 0.34 0.45

b1(WC) �1.34 0.79 0.48 9.52 54.15

b2(WC) �1.73 �20.58

b3(WC) 0.13 2.82

b4(WC) �0.12

Exponential regressions: a 49.49 54.56

formula 14 b(R0) �0.78 �0.83

b(T) 0.01 0.01

b(WC) �1.94

Independent variables used in

multivariate analyses

R0, T R0, T,

WC

R0, T,

WC

R0, T R0, T,

WC

R0, T,

WC

R0, T R0, T,

V

R0, V V T WC T,

WC

VM,

T

Number of variables used 2 3 3 2 3 3 2 3 2 1 1 1 2 2

R 0.82 0.82 0.82 0.90 0.91 0.94 0.59 0.60 0.21 0.17 0.66 0.95 0.76 0.97

R2 0.67 0.67 0.67 0.81 0.83 0.88 0.35 0.36 0.04 0.03 0.44 0.90 0.58 0.94

Adjusted R2 0.66 0.65 0.65 0.76 0.74 0.83 0.25 0.20 �0.15 �0.06 0.37 0.88 0.52 0.93

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vitrinite-rich coals, n = 45(Coppens, Denneberg, Gaschnitz)

0

5

10

15

20

25

30

35

40

0 1 2 3% R0

m ∞

[Std

. m3 /

t]

m ∞

[Std

. m3 /

t]

meas.calc.linear trend (50oC, 0%WC)

meas.calc.linear trend (2%R0, 0%WC)

vitrinite-rich coals, n = 45(Coppens, Denneberg, Gaschnitz)

0

5

10

15

20

25

30

35

40

0 50 100 150 200

T [oC]

Fig. 4. Measured and calculated Langmuir monolayer sorption capacity (ml, dry ash-free basis) calculated as a function of vitrinite reflectance,

temperature and water content. Data from Coppens (1967), Gaschnitz (2000), and Denneberg (1998).


decay constant k. These sorption measurements per-

formed on vitrinite-rich coal contain, however, only

few measurements at different moisture contents and

the regression results should be considered with

caution. Because the k-values from these latter

data sets appeared relatively high in comparison to

the results from data set 4, it was decided to per-

form additional regressions with a fixed value of

0.6 for the decay constant k (cf. Table 10).

vitrinite-rich coals, n = 16(Denneberg)

0

5

10

15

20

25

40 50 60 70 80 90 100

Vitrinite content [%]

m ∞

[S

td. m

3 /t]

meas.calc.linear trend (2%R0, 100oC)

Fig. 5. Measured and calculated Langmuir monolayer sorption

capacity (ml, dry ash-free basis) calculated as a function of vitrinite

reflectance, temperature and vitrinite content (data from Denneberg,

1998).

As outlined above, information found in the pub-

lished literature on the influence of maceral compo-

sition (either vitrinite, or liptinite content) on the

Langmuir parameter ml is not conclusive but a

general consensus appears to exist that methane

sorption capacity is positively correlated with vitri-

nite content. On the other hand, data set 3 reveals

that liptinite and inertinite are negatively correlated

with sorption capacity. However, a general and reli-

able regression equation relating methane sorption

capacity to the proportions of the main maceral

components (vitrinite, inertinite, liptinite) is not

available. As a first approximation, a regression

function using only the vitrinite content is given in

Table 10. Further systematic experiments on indivi-

dual macerals at a broader range of different maturity

levels are required to establish the corresponding

relationships.

With the exception of the data set of Denneberg (3)

the coefficient b(R0) relating maximum sorption capa-

city and maturity was found to be positive. However,

the maturity range of data set 3 is too small

(R0=0.89–1.1% R0) to establish a clear maturity

trend.

The results of the regressions for ml based on data

sets 1 and 5 have been used for the calculations

presented in the following sections. Data set 1

(n =45) covers a wide range of maturity (0.86 to

2.74% R0) and experimental temperatures (20 and

125 8C). The maximum moisture content is 4.7%.

sapropelic coals, n = 6(Gaschnitz)

0

5

10

15

20

25

30

35

40

45

0 5 10 15WC [%]

m ∞

[S

td. m

3 /t]

calc. (50oC)meas

m∞= r * exp(-k • WC / WCcrit) + (1- r)

WCcrit = 2.8

with WCcrit = 2.35 * R0^(-2.37)

r = 0.40

k = 0.6

Fig. 7. Decrease of maximum sorption capacity with increasing

water content derived from measurements on sapropelic coals

(data set 4: Gaschnitz, 2000).


The data from Arets et al. (1962) (data set 5) are used

to represent the U-shaped ml vs. maturity trend.

These data cover a maturity range from 0.75% to

3.2% R0 and a temperature interval from 24 to 50 8C.

5.3. Langmuir parameter KL

As a first approach to derive a prediction model for

the Langmuir parameter KL, a linear multiple regres-

sion equation was used for all data sets (Fig. 8):

KL ¼ aþ b R0ð Þd R0 þ b Tð Þd T þ . . . ð12Þ

For data set 2 a better fit could be achieved when

the linear form was extended by logarithmic expres-

sions for the parameters R0 and T:

KL ¼ aþ b log R0ð Þð Þd log R0ð Þ þ b log Tð Þð Þd log Tð Þþ . . . ð13Þ

At high maturities and low temperatures the linear

regression of data set 1 predicts negative KL-values

for R0N0.03 d T+1.41 (Fig. 9). Therefore the follow-

ing exponential form was chosen as a prediction

equation:

KL ¼ ad exp b R0ð Þd R0ð Þd exp b Tð Þd Tð Þð Þ þ bd WC:

ð14Þ

The dependence of KL on the water content was

derived from data set 4 (sapropelic coals, 50 8C, n =6,

vitrinite-rich coals, n = 15(Arets)

5

10

15

20

25

30

35

0 10 20 30 40 50VM [%]

m ∞

[S

td. m

3 /t]

calc.meas.polyn. trend (40oC)

Fig. 6. Relationship between the Langmuir monolayer sorption capacity

Gaschnitz, 2000). Here a polynomial trend was found

to yield the best fit:

KL ¼ aþ b WCð Þd WCþ b2 WCð Þd WC2 þ . . .

ð15Þ

The results of the regression analyses are sum-

marized in Table 11. For sample set 3 (Denneberg,

1998) the KL parameter of the Langmuir formula

does not show a clear maturity trend. The maceral

vitrinite-rich coals, n = 15(Arets)

m ∞

[S

td. m

3 /t]

5

10

15

20

25

30

0 20 40 60 80T [oC]

calc.meas.linear trend (20%VM)

(ml), maturity and temperature (data set 5: Arets et al., 1962).

vitrinite-rich coal, n = 15(Arets)

0.0

0.5

1.0

1.5

0 10 20 30 40 50

VM [%]

KL [

MP

a]

vitrinite-rich coal, n = 15(Arets)

0.0

0.5

1.0

1.5

0 20 30 40

T [oC]K

L [

MP

a]

meas.calc.linear trend (20%VM)

meas.calc.linear trend (40oC)

Fig. 8. Relationship between KL, maturity and temperature (data set 5: Arets et al., 1962).


composition does not show a significant influence

either.

The calculations in the following chapters refer

to the multiple regression results from data set 1

and 5.

6. Sorption capacity as function of depth

Coal seams in sedimentary basins exhibit speci-

fic maturity–depth trends resulting from the thermal

vitrinite-rich coal, n = 45(Coppens, Denneberg, Gaschnitz)

0

2

4

6

8

10

12

14

16

0 1 2 3 4

% R0

KL [

MP

a]

K [

MP

a]

meas.calc. (exp.)linear trend (30oC, 0%WC)exp. trend (30oC, 0%WC)

Fig. 9. Plot of KL versus vitrinite reflectance and temperature (data set 1

exponential regression trends are shown for 30 8C and 2% R0, respective

history experienced during burial and uplift. Using

the relationships defined above, the Langmuir para-

meters for individual coal seams in a sedimentary

sequence may be calculated based on their maturity

(R0 or VM) and the prevailing subsurface tempera-

ture (T). Taking into account the fluid pressure

(e.g. hydrostatic pressure gradients), adsorption

capacity vs. depth profiles can be drawn for the

entire set of coal seams in the basin. For the

present investigation on the Campine Basin, a

hydrostatic pressure gradient of 10 MPa/km and a

L

meas.calc. (exp.)linear trend (2%R0, 0%WC)exp. trend (2%R0, 0%WC)

vitrinite-rich coal, n = 45(Coppens, Denneberg, Gaschnitz)

0

2

4

6

8

10

12

14

16

0 50 100 150 200

T [oC]

: Coppens, 1967; Gaschnitz, 2000; Denneberg, 1998). Linear and

ly, and zero water content (WC).

0

1

2

3

4

5

6

0 5 10 15 20 25 30

sorption capacity [Std. m3/t]according to dataset 1

dep

th [

km]

2 % R01.5 % R01 % R00.6 % R00.6-1.6 % R0

Fig. 10. Methane sorption capacity of coal seams as a function of depth determined according to the formula defined on the basis of the

data set 1 (Coppens, 1967; Denneberg, 1998; Gaschnitz, 2000). The pressure and temperature gradients are 10 MPa/km and 35 8C/km,

respectively.


geothermal gradient of 35 8C/km were assumed.

Methane sorption capacity/depth trends were calcu-

lated according to the regression equations derived

from data sets 1 and 5:

Data set 1 (0.86–2.74% R0, 20–175 8C, 0–4.7

wt.% WC):

ml R0; Tð Þ ¼ 23:68þ 4:84d R0 � 0:09d T ð16Þ

KL R0; Tð Þ ¼ 49:50d exp � 0:78d R0ð Þd exp 0:01d Tð Þð17Þ

ml R0; T ;WCð Þ ¼ 27:44þ 3:26d R0 � 0:10d Tð Þd 0:46d exp � 0:6d WC=1:9ð Þ½þ 1� 0:46ð Þ� ð18Þ

KL R0; T ;WCð Þ ¼ 54:56d exp � 0:83d R0ð Þðd exp 0:01d Tð ÞÞ � 1:94d WC: ð19Þ

Data set 5 (0.75–3.2% R0, 5–40% VM, 25–50 8C):

ml VM; Tð Þ ¼ 35:58� 1:16d VMþ 0:02d VM2

� 0:08d T ð20Þ

KL VM; Tð Þ ¼ 0:71þ 0:10d VMþ 0:13d T : ð21Þ

The sorption capacity trends shown in Figs. 10 and

11 were calculated as functions of temperature, pres-

sure and for a given maturity/depth trend also shown

in the diagram (grad(R0)=0.2% R0/km, starting at

0.6% R0). Due to the predominating effect of pressure

the sorption capacity/depth trends increase with depth

initially, pass through a maximum and then decrease

due to the influence of increasing temperature at

greater depth. In Figs. 10 and 11 the maximum sorp-

tion capacity is reached at approximately 1000 and

500 m, respectively. The influence of maturity is

significantly different in the two examples. In Fig.

10 (data set 1) the curves shift continuously towards

higher adsorption capacities with increasing maturity.

The relationship of data set 5 (Arets et al., 1962)

shown in Fig. 11 predicts high sorption capacities in

the low and high maturity regions with a minimum

around 1% R0.

The influence of moisture content on gas sorption

capacity is shown in Fig. 12. The reduction has been

calculated using constants k =0.6, r =0.46, and

WCcrit =1.9 in formula (18) and b(WC)=�1.94 in

formula (19). It is evident that the maximum reduction

0

1

2

3

4

5

6

0 5 10 15 20 25

sorption capacity [Std. m3/t]according to dataset 5

dep

th [

km]

2 % R01.5 % R01 % R00.6 % R00.6-1.6 % R0

Fig. 11. Methane sorption capacity as a function of depth determined according to the formula defined on the basis of the data from Arets et al.

(1962). The pressure and temperature gradients are 10 MPa/km and 35 8C/km, respectively.


is up to 50%. However, taking into account the aver-

age water content in Fig. 2, which is approximately

1.5 wt.% at 1% R0, a reduction by ~20% should be

expected for the Campine Basin.

0

1

2

3

4

5

6

0 5 10

sorption capacaccording t

dep

th [

km]

Fig. 12. Methane sorption capacity as a function of depth and water content

1 (Coppens, 1967; Denneberg, 1998; Gaschnitz, 2000). The computatio

temperature gradients are 10 MPa/km and 35 8C/km, respectively.

7. Sorption capacity as function of burial history

Based on the relationships derived in this study, the

methane sorption capacity of coal seams can be cal-

15 20 25

ity [Std. m3/t]o dataset 1

WC = 0 %WC = 1 %WC = 2 %WC = 3 %WC = 4 %WC = 5 %

determined according to the formula defined on the basis of data set

n is based on a vitrinite reflectance of 1% R0, the pressure and


culated as a function of burial and maturation history,

i.e. the dynamic evolution in terms of rank, tempera-

ture and pressure throughout the geological history. In

Fig. 13 the effect of burial history on the methane

sorption capacity is sketched for three key events: the

stage of maximum burial, the situation after uplift and

erosion and the present-day situation. For simplicity,

all sorption capacity curves in Fig. 13 were calculated

for uniform vitrinite reflectance (1% R0).

Changes in adsorption capacity through time have

been outlined for well KB206. The evolution of the

Campine Basin is characterized by two major burial

phases during Carboniferous and Triassic/Jurassic

times. During maximum burial (300 Ma), the top of

the coal bearing sequences was located at a depth of
d
epth

[m

]

25

0 10 20 30

mads [Std. m3/t]300 Ma

0

500

1000

1500

2000

2500

3000

3500

dep

th [

m]

erodedWestphalian C, D

coal layers

1

0

500

1000

1500

2000

2500

3000

3500

0 10 20 30mads [Std. m3/t]

dep

th [

m]

present

unchangedadsorptioncapacity

23

additionaladsorptioncapacity

loss inadsorptioncapacity

1

Fig. 13. Sorption capacity profiled for the coal-bearing sequences of well K

is ca. 1300 m.

approximately 1900 m (Laenen et al., in preparation).

Subsequent uplift moved the top coal layers to a depth

of 60 m below the erosion surface approximately 250

Ma ago (present Westphalian/Permian discontinuity)

and 180 m below the erosion surface about 90 Ma ago

(present Triassic/Cretaceous discontinuity). After re-

burial to the present depth the uppermost coal seam

occurs at 860 m below the present surface.

Fig. 13 shows three sorption capacity/depth dia-

grams representing the key events at 300 Ma, 250 Ma

and present. Each plot includes both the actual and

the previous sorption capacity trends numbered

according to the key events. Additionally, the depth

ranges of the coal bearing sequences are marked. The

plot reveals alternating ad- and desorption cycles with

0

500

1000

1500

2000

2500

3000

3500

0 10 20 30

mads [Std. m3/t]0 Ma

unchangedadsorptioncapacity

additionaladsorptioncapacity

12

desorption

Westphalian/Permianerosion surface

KB206:

The adsorption capacity has beencalculated for two sedimentation (left) andone erosion cycle (right).For simplification the adsorption trend corresponds to that of a coal with a constantmaturity of 1.0 %R0 (trend according todataset 1).

1) adsorption capacity before erosion

2) adsorption capacity after erosion

3) present-day adsorption capacity

coal layers

B206 illustrated for three major events; the drilled depth of this well


changing burial depth. Depending on the burial his-

tory and the position within the sedimentary column,

the maximum methane sorption capacity of a coal

seam may either increase or decrease. It is evident

from Fig. 13 that after the first uplift/erosion event

(250 Ma) the sorption capacity of the top layers

decreased, whereas for the deeper sequences an

increase in sorption capacity is observed. After re-

burial (present, curve 3), and in comparison to the

adsorption capacity established at 250 Ma (curve 2)

this trend is reversed and the sorption capacity of the

top layers has increased, whereas the deeper

sequences have lost part of their sorption capacity.

At depth levels below ca. 1500 m the first adsorption

capacity curve established at 300 Ma (curve 1) exhi-

bits the lowest sorption capacity.

During subsidence and increasing burial, the

excess gas stored in the lowermost coal seams is

desorbed and migrates upwards, where it either re-

adsorbs in undersaturated layers having a higher sorp-

tion capacity, is trapped underneath low-permeable

layers or will migrate to the surface. Zones where

additional sorption capacity is created will remain

undersaturated if no additional gas is supplied.

ad- and desorbed amount of gas [Std. m3/t, daf]

0 5 10 150.8

1.0

1.2

1.4

0.6 0.8 1.0 1.2

% R0

dep

th [

km]

Fig. 14. Comparison of measured gas contents from canister desorption te

capacities were either calculated according to Stuffken (1960) and the re

Gaschnitz, 2000). The regressions were used to calculate different adsorp

during maximum burial (300 Ma), (2) after erosion (250 Ma) and (3) the p

calculated based on the water contents measured on samples of well K

WCcrit=1.8–3.8%), both decreasing with depth. Error bars indicate a 50%

For the quantification of the present-day gas con-

tent of coal seams one has therefore to take into

account the gas generation history:

— Assuming that no additional gas (e.g. late-stage

biogenic gas) has been generated or supplied from

greater depth after the time of maximum burial, the

gas storage potential of the coal seam in all sub-

sequent periods will be equal to the lowest sorp-

tion capacity which has been exhibited since (zone

of dunchanged adsorption capacityT in Fig. 13).

— If the layers, where additional sorption capacity

was created during the geological history, are sup-

plied with appropriate amounts of thermogenic gas

from deeper sequences or secondary biogenic gas,

the present-day adsorption trend has to be used for

the gas content computations.

8. Coalbed methane content of well Peer KB206

Figs. 14 and 15 show comparisons of gas contents

from canister desorption tests with calculated sorption

capacities for the Peer KB206 test well (RRRI and

(well KB206)

20 25

desorbed gas (DMT)desorbed gas (RRRI)calc. after Stuffken (1960)

123min. ‘dry’ sorption capacity

min. ‘wet’ sorption capacity(k = 0.6, r = 0.46)min. 'wet' sorption capacity(k = 5.24, r = 0.36)

calc.dataset 1

sts and calculated adsorption capacities for well KB206. Adsorption

gressions based on data set 1 (Coppens, 1967; Denneberg, 1998;

tion trends (dry state), which either follow (1) the one established

resent-day burial depth. The reduction due to moisture content was

B206 and the calculated critical water content (WC=1.4–2.0%,

reduction of the minimum ddryT sorption capacity.

ad- and desorbed amount of gas (well KB206)[Std. m3/t, daf]

0 5 10 15 20 25

desorbed gas (DMT)desorbed gas (RRRI)calc. after Stuffken (1960)123min. ‘dry’ sorption capacitymin. ‘wet’ sorption capacity(k = 0.6, r = 0.46)

min. ‘wet’ sorption capacity(k = 5.24, r = 0.36)

calc.dataset 5

calc.dataset 1

0.8

1.0

1.2

1.4

0.6 0.8 1.0 1.2

% R0

dep

th [

km]

Fig. 15. Comparison between the measured desorption data and calculated adsorption capacities for well KB206. Adsorption capacities were

either calculated according to Stuffken (1960) and the regressions based on data set 5 (Arets et al., 1962). The reduction due to a certain

moisture content has been taken from the regression results of data set 1. The regressions were used to calculate different adsorption trends (dry

state), which either follow (1) the one established during maximum burial (300 Ma), (2) after erosion (250 Ma) and (3) the present depth profile.

Error bars indicate a 40% reduction of the minimum ddryT sorption capacity.


DMT), which covers a maturity interval from 0.85%

to 1.14% R0 (28–38% VM). The plots also include the

gas content data calculated according to the dStuffkencurveT which has been established empirically for

coals from the Campine Basin (Stuffken, 1960). Addi-

tionally, the plots include several calculated sorption

capacities, which are based on the regression equation

derived from data set 1 (Coppens, 1967; Denneberg,

1998; Gaschnitz, 2000; Fig. 14) and the regression for

data set 5 (Arets et al., 1962; Fig. 15). According to

formulas (16)–(21) the curves are calculated as a

function of pressure, temperature, maturity and

water content:

— Methane sorption capacity trend (dry state) estab-

lished at maximum burial (~300 Ma ago) (d1T inFigs. 14 and 15).

— Methane sorption capacity trend (dry state) estab-

lished after erosion (~250 Ma ago) (d2T in Figs. 14

and 15).

— Present-day methane sorption capacity trend (dry

state) (d3T in Figs. 14 and 15).

— the minimum ddryT sorption capacity, assuming

that no gas has been supplied after maximum

burial.

— Two curves for the predicted minimum bwetQ sorp-tion capacity calculated according to formulas

(18)–(19) (with k =0.6, r =0.46), as well as the

best fit trend, established for data set 1, where ml

is given as a function of k =5.24, r=0.36 (Table 10).

The bwetQ sorption capacity is based on measured

water content data from well KB206 (WC=1.4–

2.0%) and the calculated critical water content

according to Gaschnitz (2000) (WCcrit =1.8–3.8%).

In comparison to the present-day adsorption capa-

city (curve 3) both figures (Figs. 14 and 15) indicate

lower adsorption capacities in the upper part of the

coal layers for the minimum adsorption capacity

trend, which is convenient with the trend of the des-

orption data. This trend would correspond with the

suggestion that no or insufficient secondary gas was

supplied after the time of maximum burial. Isotopic

analysis of the methane produced in well KB206

supports some microbial origin. The isotopic data

(dD~178x, d13C~59x) indeed plot within the tran-

sitional thermogenic/biogenic gas zone (Wenselaers et

al., 1996).

When taking the water content into account (mini-

mum bwetQ adsorption capacity) it is shown that the


calculated amount of gas appears to be overestimated

by up to 10 Std. m3/t in comparison to the desorption

data. The only match with the canister desorption data

is given in Fig. 15 for the data below ca. 1100 m,

computed with k =5.24 and r =0.36. In order to fit the

measured desorption data, the ddryT adsorption capa-

cities in Figs. 14 and 15 would have to be reduced by

50% to 60% and 30% to 55%, respectively. The

calculated reduction due to moisture content does

not predict such a strong decline.

In general, an undersaturation may be the result of

diffusive gas loss from coal seams into adjacent

permeable formations (aquifers). Removal of gas in

solution or in a free phase will decrease the gas partial

pressure around the coal seams and result in contin-

uous gas loss. Therefore, the calculated adsorption

trends must be considered as an estimation of the

maximum sorption capacity of moist coals in equili-

brium with a free methane phase. Fluid movement has

previously been considered a potential cause for gas

production problems at test well Peer KB206. Due to

a strong water influx during pumping, which appeared

to be related to a nearby fault zone, the fluid pressure

in the well could not be reduced below a certain

pressure (Wenselaers et al., 1996) which resulted in

a limited gas production. Thus, the active hydrody-

namic regime may have caused gas loss from the

adjacent coal seams and thus undersaturation of the

coals.

9. Conclusions

Sorption capacity denotes the maximum amount of

gas that can be physically stored in coal seams at

given subsurface conditions. It is basically influenced

by pressure and temperature, the actual moisture con-

tent, the composition of the organic material, the

mineral content, and the coal rank of the sediments:

— Sorptive gas storage capacity increases with

increasing gas pressure and decreases with increas-

ing temperature.

— Sorption capacity is positively correlated with coal

rank. The data from Coppens (1967) show a linear

increase in sorption capacity with increasing coal

rank. The data by Arets et al. (1962) indicate that

the amount of adsorbed gas is highest for mature

samples and passes through a minimum for coals

with a volatile matter content of 27% (~1.2% R0).

— Measurements performed on dry material and at

different water contents revealed that even very

low water contents (e.g. 1.8 wt.%) lead to a strong

reduction of the adsorption of methane. A decrease

of sorption capacity up to 50% has been measured.

— With respect to the maceral composition the data

sets used in this study support two different trends:

Denneberg (1998) showed that sorption capacity

increases with increasing vitrinite content and

decreases with liptinite and inertinite content.

Gaschnitz (2000) demonstrated that there is a dis-

tinct difference in adsorption capacity between

macrolithotypes. Highest storage capacities have

been measured for sapropelic coals, intermediate

capacities for vitrain samples and lowest for iner-

tinite-rich coals.

Using multiple regression procedures, expressions

have been defined for different data sets, which take

into account the key parameters influencing the

adsorption capacity of coals. Based on the Langmuir

formula the adsorption capacity can be estimated with

reasonable accuracy as a function of pressure, tem-

perature, maturity, maceral composition and water

content. Due to the limited database, the expressions

relating sorption capacity to maceral composition and

water content are not yet very reliable. Among the

regression equations derived in this study, those based

on data sets 1 and 5 were applied in the subsequent

calculations of the evolution of sorption capacity with

burial history of the coal seams:

1) Data set 1, n =45 (0.86–2.74% R0, 20–175 8C):

ml R0; Tð Þ ¼ 23:68þ 4:84d R0 � 0:09d T

KL R0; Tð Þ ¼ 49:50d exp � 0:78d R0ð Þd exp 0:01d Tð Þ

2) Data set 5, n =15 (0.75–3.2% R0, 5–40% VM, 25–

50 8C):

ml VM; Tð Þ ¼ 35:58� 1:16d VMþ 0:02d VM2

� 0:08d T

KL VM; Tð Þ ¼ 0:71þ 0:10d VMþ 0:13d T :

Symbol Unit Definition

n – Number of measurements

WC wt.% Water/moisture content

WCcrit wt.% Critical water/moisture

content

V % Vitrinite content

T 8C Temperature

R0 % Vitrinite reflectance

VM % Volatile matter

a, bx(. . .) – Constants determined by

regression for the linear

influence of R0, T, . . .

k, r – Constants determined

by regression for the

influence of WC


In order to estimate the present-day coalbed methane

quantities in the Campine Basin, a simplified burial and

gas supply history consisting of three major events was

considered: (1) period ofmaximum burial, (2) the period

after maximum uplift/erosion and (3) re-burial to the

present depth. Assuming that no additional gas (e.g. late-

stage biogenic gas) was generated after the time of

maximum burial, the gas sorption capacity of a given

coal seam is characterised by the lowest value it has

reached subsequently. If the coal layers, where addi-

tional sorption capacity was created during the geologi-

cal history, are supplied with appropriate amounts of

thermogenic gas from deeper sequences or secondary

biogenic gas, the present-day sorption capacity trend has

to be used for the computation of storage capacities.

The present gas content observed in well Peer

KB206 may be explained by two likely processes:

1) No or not sufficient secondary gas was supplied

after the time of maximum burial. This would

explain the low gas content values in the upper-

most coal sequences.

2) Migration of gas occurring as a result of fluid

circulation along a nearby fault system (Donder-

slag fault) led to the low gas contents measured at

well KB206.

The mathematical expressions provided in this

manuscript seem to represent adequately our current

observations in the Campine Basin. However, extend-

ing these relationships to other basins requires more

detailed work.

10. Symbols and units

Throughout this study the volume of gas is

reported at standard conditions [Std. m3/t] at 0 8C(273.15 K) and 0.101325 MPa (1 atm).

Symbol Unit Definition

mads Std. m3/t Mass of adsorbed

gas at pressure p

ml Std. m3/t Maximum amount

of adsorbed gas at

complete surface

coverage

p MPa Pressure

KL MPa Langmuir pressure

Acknowledgements

This study is part of a work package within the

research program commissioned by the Flemish

Department of Natural Resources and Energy

(ANRE). The authors wish to thank the following

persons for helpful discussions and suggestions: the

colleagues at VITO (Ben Laenen, Peter van Tongeren)

and Michiel Dusar (Belgium Geological Survey, Brus-

sels). Measurements on samples from Peer KB206

have been performed in 1993 by: Deutsche Montan

Technologie fur Rohstoff, Energie, Umwelt, Institut

fur Bewetterung und Klimatisierung (DMT) and

Raven Ridge Resources Incorporated, Exploration

and Development of Energy Resources, Grand Junc-

tion, Colorado 81505 (RRRI).

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