Estimating Average Total Cost of Open Pit Coal Mines in
Australia
1
2
S Shafiee , M Nehring and E Topal
3
ABSTRACT
In preliminary feasibility studies mining project evaluators seek to estimate capital and
operating costs of a proposed mining venture before mine layout design and general
plant design. Mine planners often need to provide guidance on probable mine
feasibility with insufficient cost information. There are two main methods for
preliminary cost estimation. The first one relates to a few available sources that
calculate the required cost information. The Mine and Mill Equipment Cost Calculator
is an online exhaustive software tool which compiles capital and operating cost data
for mining and milling purposes. Mine Cost is another online source which provides
mine cost spreadsheets and operating cost information based on verifiable engineering
and production data. The main shortcoming of these online sources is that they do not
provide any detail about the methods for estimating mining cost and simply calculate
total cost for a specific mine. The second one, which is more recognisable in industry,
derives preliminary cost estimates by computing average total costs (ATC) of existing
mining projects and operations. Since each mine has unique specifications, cost
information should be estimated individually. This study focuses on establishing a
range of ATCs according to previous mining projects and indices.
This study endeavours to estimate an index and econometrics model for Australian
coal mines to guide mining project evaluation to find the ATC in open pit coal mines. In
other words, this paper reviews about 20 Australian open pit existing coal mines
operated by different companies and categorises them by size of mine and deposit, coal
quality parameters and production rate. It then, computers the ATC according to these
existing open pit coal mines. After that, the ATC is expanded for the next 30 years by
specific mining and milling indices. The study introduces an econometrics model to
estimate mining and milling cost index via the Marshal and Swift equipment cost index
for the future. Finally, this research provides an appropriate ATC for mine life which
could be useful for preliminary feasibility studies of mining projects to find out general
site conditions, mining methods, and milling processes.
INTRODUCTION
In mining project evaluation and decision making, the prediction of future volatility is a key input
parameter. Capital and operating cost estimation on any proposed mining project is essential to evaluate
a mining project for the whole of its mining life. Nowadays new project evaluation methods such as the
1.
Postgraduate Student School of Engineering, CRC Mining, The University of Queensland, St Lucia Qld 4072.
Email: s.shafiee@uq.edu.au
2.
Postgraduate Student School of Engineering, CRC Mining, The University of Queensland, St Lucia Qld 4072.
Email: m.nehring@uq.edu.au
3.
Associate Professor/Head of Mining Engineering Department, Western Australia School of Mines, Curtin University of
Technology. Email: e.topal@curtin.edu.au
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S SHAFIEE, M NEHRING and E TOPAL
real option valuation (ROV) technique use cost estimation as a function to appraised mining projects
(Shafiee and Topal, 2008; Shafiee, Topal and Micha, 2009). At the beginning of cost estimation, it is
very difficult to find valuable data in all phases that project evaluators can use in their cost estimation.
These data include a mining cost model, electric power costs, mining equipment costs, pertinent
geologic, mineralogical, metallurgical variables, wages and benefits, cost indices, plus smelting and
tax information (InfoMine, 2009). The economic evaluation components are based on information of
the feasibility study, but engineers preparing feasibility studies never have all the engineering and
economic information that is needed for accurate mining cost estimation. For instance, a small change
in overburden and ore transportation costs in an open pit mine could have a significant effect on the
cost that would shift the location of aggregate production away from the original location (Jaeger,
2006). Therefore, mining data plays a key role in mining project development to clearly feasible,
doubtfully feasible or clearly uneconomic (Gentry and O’Neil, 1984).
Most of the cost estimations and mining financial assets use past volatility in other mining projects
as proxy for their cost estimation. Nevertheless, in practice this approach is not feasible because,
historical data of some mining projects are not publicly released. This means volatility needs to be
input as a variable in commodity price and operating cost measuring. For example, Lima and Suslick
found that mining project volatility is independent of production capacity and depends on increment
in price and cost (Lima and Suslick, 2006). Consequently, as an alternate solution each company use
their own data sets and estimated operating costs according to previous similar projects.
This paper initially discusses how mine cost can be calculated in two popular online sources:
‘Mine and Mill Equipment Cost Calculator’ and ‘Mine Cost’. Mining cost estimation using equations
are also discussed. Moreover, the research introduces vital indices in different sections of mining
projects and discusses their historical movements. The operating cost in 20 mines in Australia and
some mines in Canada and the US are compared. Then according to important variables and reliable
data, an econometrics model is estimated to calculate average operating cost. Finally, the estimated
operating cost by econometrics model is validated using InfoMine operating cost estimation.
HOW IS MINE COST CALCULATED?
Uncertainty is attached in all the variables in mining project evaluation. For this reason, in order to
take a conservative approach the mining project evaluator may tend to overestimate operating cost of
the mine. High stripping ratio, seams with complex metallurgy characteristics, mines located in
isolated regions, bad climate, lack of access to roads, electricity and water supply as well as
mountainous topographic challenges, all contribute to uncertainty in the project. As projects move
forward and greater certainty about crucial variables becomes more important, operating costs should
be revised. For instance, knowledge of the deposit in regards to electricity and water consumption as
well as metallurgical characteristics can be assessed through borehole sampling. A more accurate
estimate of overall project capital cost and operating cost can be made from a summation of items of
costs after judging the local specific effect conditions on each item of capital and operating cost.
Another main variable affecting capital and operating cost is the process plant throughput rate.
The greatest influence over mine revenue is produced by production rate, although, other variables
such as coal quality, stripping ratio, as well as other possible explanatory variables is less important.
Estimation of the daily run-of-mine is based on operating time periods of the process plants, mining
operations, crushing plants and maintenance services. There is a general agreement that the size of the
deposit has a strong influence on production rate (Smith, 1997), even though coal reserves are
available up to 2112, and will be the only fossil fuel remaining after 2042 (Shafiee and Topal, 2009).
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This means that the ratio of coal production to coal reserve is very low in comparison to other
non-renewable resources.
There are two main methods for preliminary cost estimation. The first one is an online software
called ‘Mine and Mill Equipment Cost Calculator’ that calculate all equipment cost in feasibility. This
software provides a comprehensive reliable data for equipment cost in detail. All data is based on
InfoMine US’s complete equipment cost database, along with the mechanism to extract and adjust the
data for specific projects. InfoMine US is dedicated to providing the cost data and advice mining
projects need to make informed mining decisions. This software draws on its extensive background in
mine planning, cost estimation, and project evaluation to understand mining projects’ cost data
(InfoMine, 2009). The second one is ‘Mine Cost’, which is an online source that provides mine cost
spreadsheets and operating cost information based on verifiable engineering and production data.
This site is a source of mine cost spreadsheet models and operating cost information based on
verifiable engineering, production data and peer reviewed by mining industry analysts. It presents
some spreadsheet model containing detailed production and mine economics data, in many cases back
to 1991 as well as projections for future years up to 2012. All models have cost curves showing the
operation’s comparative cost ranking against all other mines in the industry (Mine Cost, 2009).
Consequently, these two available methods estimate mining cost based on their own data bases, then
provide an overview for future mining. The main shortcoming of these two methods is that they do not
provide any details about the methods for estimating mining cost and simply calculate total cost for a
specific mine. Since each mine has unique specifications, then cost information should be estimated
individually. This study focuses on establishing a range of ATCs according to previous mining
projects and available indices.
Some studies according to previous mining projects data generate an exponential function for
daily production and operating cost. The set of Equation 1 shows some of the main equations that
were estimated in 1980 by O’Hara based on his data collection (O’Hara, 1980):
Capital cost (US$ M) = $400 000 (tons mined and milled daily) 0.6
(1)
Stripping cost (US$ M) = $800 (millions tons of overburden soil)0.5
Stripping cost (US$ M) = $8500 (millions tons of overburden rock)0.5
Equipment cost (US$ M) = $6000 (tons of deposit and waste mined
daily)0.7 + $5000 (tons of deposit and waste mined daily)0.5
Maintenance cost (US$ M) = $150 000 (tons of deposit and waste
mined daily)0.3
Labour cost (US$) = $58.563 (tons of deposit and waste mined daily)-0.5
+ $3.591 (tons of deposit and waste mined daily)-0.3
Supplies cost (US$) = $13.40 (tons of deposit and waste mined daily)-0.5
+ $1.24 (tons of deposit and waste mined daily)-0.3 + $0.90 (tons of deposit
and waste mined daily)-0.2
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The main shortcoming of O’Hara’s estimation relates to the expansion of Equation 1 over the
mining project life; however, these equations are still one of the best approaches in cost estimation
literature. This paper estimates cost function according to some of O’Hara’s variables.
COST INDICES FOR MINING PROJECTS
Cost indices in the mining industry provide a general overview and a mechanism for adjusting
outdated cost information. One of the advantages of cost indices is that they show the effect of
inflation on mining costs. Figures 1 to 4 illustrate a variety of crucial indices for specific mining
scenarios as computed by InfoMine in 2009 (CostMine, 2009). These indices are measured based on
general inflation in the US economy. InfoMine claims those indices serve as a comparison to the
inflation experienced by the mining industry. This section will discuss the fluctuations in cost indices
from 1965 to 2008.
Figure 1 illustrates two labour indices ‘mine and mill operation labour’ and ‘mill construction
labour’. Both labour indices move together throughout the periods. As shown the labour index over
the last 40 years has increased by around six times. Figure 2 depicts ‘machinery, heavy equipment,
repair parts’, ‘trucks tyres, scrapers, loaders, etc’ and ‘line haul railroads (all services)’ from 1965 to
2008. The trends of these three indices were similar to each other from 1965 to 1982. After 1982
‘machinery, heavy equipment, repair parts‘ and ‘line haul railroads (all services)’ indices increased by
relatively the same slope as the previous period to 2008,while the 'truck tyres, scrapers, loaders, etc’
index decreased until early 2000s and slightly increased in late 2000s. Figure 3 shows the trend of
‘gasoline, diesel, propane, fuel oil and lubricants’, ‘coal fuel’, ‘natural gas fuel’ and ‘electric power’.
As can be seen in Figure 3 ‘gasoline, diesel, propane, fuel oil and lubricants’ and ‘natural gas fuel’ got
more expensive in comparison to the two other indices. ‘Natural gas’ has risen by 50 times over the
last 40 years, this index increased the most in comparison to all other indices. Figure 4 demonstrates
five operation indices from 1965 to 2008. ‘Explosives and accessories’, ‘miscellaneous materials and
25
Mine and mill operating labour
Mill construction labour
20
15
10
5
2007
2008
2005
2006
2003
2004
2001
2002
1999
2000
1997
1998
1995
1996
1993
1994
1991
1992
1989
1990
1987
1988
1985
1986
1983
1984
1981
1982
1979
1980
1977
1978
1975
1976
1973
1974
1971
1972
1969
1970
1967
1968
1965
1966
0
FIG 1 - Mine and mill operating labour and mill construction labour for coal mining processing cost indices
(source of data: CostMine, 2009).
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FIG 2 - Machinery, heavy equipment, repair parts, tires of trucks, scrapers, loaders, etc and line haul railroads
(all services) for coal mining and processing cost indices (source of data: CostMine, 2009).
400
Gasoline, diesel, propane, fuel oil, and lubricants
Coal fuel
Natural gas fuel
Electric power
350
300
250
200
150
100
50
2007
2008
2005
2006
2003
2004
2001
2002
1999
2000
1997
1998
1995
1996
1993
1994
1991
1992
1989
1990
1987
1988
1985
1986
1983
1984
1981
1982
1979
1980
1977
1978
1975
1976
1973
1974
1971
1972
1969
1970
1967
1968
1965
1966
0
FIG 3 - Gasoline, diesel, propane, fuel oil, and lubricants, coal fuel, natural gas fuel and electric power for
coal mining and processing cost indices (source of data: CostMine, 2009).
supplies not otherwise covered’, ‘drill bits and steel, mill balls, rods and liners, track’ and ‘mill reagents,
mining chemicals’ fluctuated together over the period. ‘Mine timber’ fluctuated the same as other
operation indices from 1965 to 1991, then after 1991 this index nearly doubled in less than four years in
1995. From 1995 to 2008 this index was relatively stable with minor oscillations. Consequently all
fourteen indices over that period increased for coal mining and processing cost.
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FIG 4 - Explosives and accessories, miscellaneous materials and supplies not otherwise covered, drill bits and
steel, mill balls, rods and liners, track mill reagents, mining chemicals and mine timber for coal mining and
processing cost indices (source of data: CostMine, 2009).
Table 1 shows some statistical calculation for all coal mining and processing indices from 1965 to
2008. The labour index in the coal mining industry is the minimum amount of mine cost while mine
timber is the maximum amount. Coefficient of variance demonstrates fuel indices had the most
volatility and the labour index had minor volatility. Entire indices increased over the time period
between six to ten times. With the exception of operating labour that increased just 40 per cent over
the last 40 years, as well as oil and natural gas fuel which soared 22 and 51 times respectively from
1965 to 2008.
The Coal Cost Guide (CCG) is an internally generated index that provides a reliable means for
tracking the cumulative effect of cost inflation on mining projects in the US and Canada (CostMine,
2009). In 2006 and 2007, the US had 802 surface and 559 underground mining activities. The largest
producer of coal in 2007 was Wyoming with 411.5 million tonnes followed by West Virginia,
Kentucky, Pennsylvania and Texas. Moreover, Alberta and British Columbia coal mines are the largest
coal producing states in Canada (CostMine, 2009). In Australia, Queensland and New South Wales are
the largest coal producers. The Hunter Valley coal operations in NSW is one of the biggest single open
pit mines in the world producing around 12 - 15 million tonnes per year. Unfortunately, there is not any
reliable data for coal mines in Australia in comparison to US and Canada. Table 2 endeavours to collect
operating cost, capital cost and production rate for 20 Australian open pit coal mines. As can be seen in
Table 2 real data for Australian mines was not readily available. However, the authors estimated a
reliable range for those variables. An estimated combined capital and operating cost was used where
expansions of projects had taken place since initial start up. The authors believe that the non-availability
of mine data is the main reason for lack of research in cost estimation in Australia. The CCG indices are
used as proxy for estimating the cost of each item in Australian surface coal mining cost.
Figure 5 depicts the CCG indices for the US and Canadian mining industries individually from
1999 to 2008 and from 1991 to 2008, respectively. The figures show that Canadian operating cost and
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ESTIMATING AVERAGE TOTAL COST OF OPEN PIT COAL MINES IN AUSTRALIA
TABLE 1
Mean, standard deviation, coefficient of variance, maximum and minimum of coal cost indices from 1965
to 2008 (source of data: CostMine, 2009).
Coal cost index names
Mean
Standard Coefficient Increasing
deviation of variance (times)
(CV)
Max
Min
15.5
Mine and mill operating labour
18
1.82
10.00
1.4
22.0
Mill construction labour
12
5.41
45.66
6.8
21.9
3.2
Machinery, heavy equipment, repair parts
102
48.90
47.74
6.8
185.4
27.2
Drill bits and steel, mill balls, rods and liners, track
99
48.68
49.04
8.5
246.8
28.9
Mine timber
122
56.86
46.73
5.4
209.8
28.9
Gasoline, diesel, propane, fuel oil, and lubricants
70
54.83
77.81
22.1
272.0
12.3
Explosives and accessories
105
49.04
46.77
6.0
199.7
32.8
Tires of trucks, scrapers, loaders, etc
80
24.39
30.64
3.3
120.8
36.2
Miscellaneous materials and supplies not otherwise
covered
98
43.97
45.00
6.2
192.4
30.9
Electric power
97
50.43
52.15
8.9
189.7
21.1
Mill reagents, mining chemicals
100
56.71
56.74
10.3
284.1
27.7
Coal fuel
82
33.05
40.11
9.0
157.7
17.5
Natural gas fuel
101
91.51
90.79
51.4
390.3
7.6
Line haul railroads (all services)
94
37.17
39.52
6.6
175.2
26.7
FIG 5 - Capital and operating cost indexes for the US and Canadian coal surface mine (source of data:
CostMine, 2009).
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TABLE 2
The main Australian surface coal mines, production rate (million tonne per annum), capital expenditure (million
Australian dollars), operational expenditure (Australian dollars per tonne), reserve and resources (million tonne)
and mine life. Source: data was taken from a variety of sources including company annual reports, company
websites and media reports (Anglo, 2009; BHP, 2009; Ensham, 2009; Felix, 2009; Gloucester, 2009; Idemitsu,
2009; Macarthur, 2009; New Hope, 2009; Peabody, 2009; Rio Tinto, 2009; Wesfarmers, 2009; Whitehaven, 2009;
Xstrata, 2009). Estimates were made on all data items that were unavailable.
No
Mine
Location
Company
Start
Prod
rate
(Mt/a)
CAPEX OPEX
(AU$ M) (AU$/t)
Reserves/ Mine life
Resources
(Mt)
1
Acland
Acland
New Hope
2003
2.7 - 3.9 110 - 170
50 - 70
109/679
2045
2
Bengalla
Muswellbrook
Rio Tinto
1999
5.5 - 6.5
50 - 65
200/369
2042
3
Blair Athol
Clermont
Rio Tinto
1984
8.0 - 12.0 190 - 230
35 - 50
50/70
2016
4
Boggabri
Boggabri
Idemitsu
2007
70 - 85
100/120
2075
5
Burton
Mackay
Peabody
1996
4.0 - 5.5 165 - 220
55 - 70
54/164
2021
1.5
450
38
6
Canyon
Boggabri
Whitehaven
2000
1.0 - 1.5
22
70 - 80
10/45
2009
7
Clermont
Clermont
Rio Tinto
2013
12.0
950
50
189/220
2030
8
Coppabella
Nebo
Macarthur
2000
2.1 - 4.2 145 - 180
55 - 75
67/207
2031
9
Curragh
Blackwater
Wesfarmers
1984
4.0 - 9.0 270 - 320
50 - 65
120/350
2025
10
Drayton
Muswellbrook
Anglo
1983
4.5 - 5.5 140 - 185
50 - 65
61/85
2021
11
Ensham
Emerald
Ensham
1994
7.0 - 9.5 340 - 290
35 - 55
600/900
2084
12
Foxleigh
Middlemount
Anglo
2000
2.5 - 3.3 130 - 160
55 - 65
60/290
2029
13
Hail Creek
Nebo
Rio Tinto
2003
5.0 - 6.0 250 - 290
40 - 60
224/420
2046
14
Jeebropilly
Amberley
New Hope
2006
0.5 - 0.85
15 - 25
60 - 75
5/7
2018
15
Mangoola
Muswellbrook
Xstrata
2012
10.5
1100
55
230/280
2033
16
Millenium
Coppabella
Peabody
2006
1.5 - 3.3
130
65 - 85
40/100
2021
17
Minerva
Emerald
Felix
2005
2.5
68
60 - 70
29/78
2020
18
Norwich P
Dysart
BHP
1979
45 - 55
120/423
2029
19
Rolleston
Rolleston
Xstrata
2005
6.0
291
49
173/600
2036
20
Stratford
Gloucester
Gloucester
1995
2.0 - 2.8
40
69
38/209
2024
4.0 - 5.5 180 - 250
capital cost mine indices are much greater than the US mine indices. Canadian operating cost index
and capital cost index movements are similar to each other from 1991 to 2003, then the operating cost
index rose more than its capital cost index since 2003. The capital cost and operating cost index in the
US increased according the same trend over the last ten years. As a result, the operating cost and
capital cost indices in both Canada and the US increased.
Figure 6 illustrates Marshall and Swift index, which is another index to represent mining and
milling equipment cost. Marshall and Swift is one of the most comprehensive databases in industrial
economics. This index does regular surveys on material prices, labour rates and equipment prices in
different industries, resulting in more than 2500 databases of location-specific costs throughout
Canada and the US Marshall and Swift provide quarterly data in mining and equipment cost indices.
This index in comparison to previous indices has advantages and disadvantages. The main advantage
of this index is availability and frequency. However, the Marshal and Swift index provides just one
figure for all subcosts in mining projects and ignores other sections of mining cost. Figure 6 illustrates
the historical movement plus an econometrics linear trend of Marshal and Swift from 1980 to 2009.
Another advantage of this index is expanding the liner econometrics trend for estimating mining and
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ESTIMATING AVERAGE TOTAL COST OF OPEN PIT COAL MINES IN AUSTRALIA
1800
MARSHALL & SWIFT EQUIPMENT COST INDEX (Mining, milling)
Linear (MARSHALL & SWIFT EQUIPMENT COST INDEX (Mining, m illing))
1500
1200
y = 6.1812x + 671.64
R2 = 0.9406
900
600
300
2009
2008
2006
2007
2004
2005
2003
2001
2002
1999
2000
1998
1996
1997
1994
1995
1993
1991
1992
1989
1990
1988
1986
1987
1984
1985
1982
1983
1980
1981
0
FIG 6 - Historical movement and liner trend of Marshall and Swift equipment cost index for mining and
milling (source of data: CHE 1980 - 2009).
milling equipment cost in the future. For example operating cost for the next ten years will be close to
the same trend of econometrics liner trend. Consequently, Figure 6 verifies again that mining and
milling cost equipment increased over that last 30 years.
OPERATING COST ESTIMATION FOR AUSTRALIAN OPEN PIT
COAL MINES
In this section the operating cost for Australian open pit coal mines is estimated according to InfoMine
data. InfoMine has computed operating and capital costs in US dollars based on American and
Canadian mines. As can be seen in Table 3, operating costs and capital costs are based on deposit
average thickness; stripping ratio and daily production rate are converted to Australian dollars.
Table 3 shows that as deposit average thickness decreases and stripping ratio increases with a fix coal
production rate, operating costs and capital costs increase. Moreover, in constant deposit average
thickness and stripping ratio as long as production rate is increasing, operating cost and capital cost
are increasing as well.
The developed econometrics model for estimating operating cost focuses on deposit average
thickness, stripping ratio, capital cost and daily production rate as the independent variables and
operating cost as dependent variable. This model has been developed to explain the influence of
independent variables on estimating operating cost. This model is estimated by ordinary least square
approach for InfoMine data in 2008 (Table 3). The result is derived by Equation 2 as follows:
EOC = 8.744955 - (0.041556) DAT + (1.658269) SR - (0.000459)
CC - (0.041408) PR
2
R = 95%, F = 60.70
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S SHAFIEE, M NEHRING and E TOPAL
TABLE 3
Surface coal mine operating cost, capital cost, production rate, deposit average thickness and stripping ratio in
different scenarios (source of data: CostMine, 2009).
Deposit average thickness (metres)
12.3
3.1
1
10.2/1
20.2/1
40.6/1
Operating cost†
31.28
49.27
84.95
Capital cost‡
60.9
105.4
213.7
Operating cost†
23.94
39.50
73.01
Stripping ratio, tonnes overburden/tonnes coal
1800
7300
Daily coal
production (tonnes)
21 800
65 300
196 000
Capital cost‡
178
196.7
354.4
Operating cost†
20.84
37.43
70.67
Capital cost‡
361.9
551.5
1026
Operating cost†
18.87
34.94
67.77
Capital cost‡
863.9
1500.8
2911.7
Operating cost†
16.95
34.25
65.68
Capital cost‡
2373.1
4348.4
8492.9
†
Operating cost is calculated based on A$ per tonne in 2008.
‡
Capital cost is computed based on million A$ in 2008.
where:
EOC
estimated operating cost (per tonne)
DAT
deposit average thickness (metres)
SR
stripping ratio (tonnes overburden per tonne coal)
CC
capital cost (A$ M in 2008)
PR
daily production rate (1000 tonnes)
The econometrics model estimates the relationship between DAT, SR, CC and PR with EOC. This
model shows that the CC and PR has a positive effect on EOC and DAT and SR has a negative effect on
EOC. For example, the model indicates that if stripping ratio increases by one tonne overburden per
tonne coal, the operating cost will be increased by a factor of 1.66 Australian dollars per tonnes, ceteris
paribus. Table 4 depicts, EOC based on econometrics model and compared with operating cost (OC)
calculated by InfoMine and Australian coal mine in Table 2. As can be seen in Table 4, the model
estimation has ±20 per cent range of accuracy with real data. Additionally, the developed model has
95 per cent significance and neither multicollinearity, heteroscedasticity nor autocorrelation problems.
CONCLUSION
In the mining industry most of the cost estimations are based on past volatility in other mining projects
as proxy for current mining projects. Moreover, uncertainty is attached in all the main variables in
mining project evaluation for estimation. As projects move forward and certainty about crucial
variables needs to increase, operating cost should be revised. For this reason, in order to take a
conservative approach the mining project evaluator may tend to overestimate operating cost of the
mine. The accuracy of estimation of capital costs and operating costs depend on the quality of the
technical assessment and knowledge of expected mining and mineral processing conditions.
Consequently, mining companies extensively investigate alternative available methods and reliable
data with intention of reducing cost.
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TABLE 4
Surface coal mine deposit average thickness, stripping ratio, capital cost, production rate, InfoMine operating cost
estimation, econometrics operating cost estimation and validity range of two estimations (source: data collected
from InfoMine, 2009 and modellings computed in Eviews).
DAT
SR
CC
PR
OC
EOC
Validity range
12.3
10.2
60.88
1.8
31.28
25.05
-20%
3.1
20.2
105.37
1.8
49.27
41.99
-15%
1
40.6
213.75
1.8
84.95
75.86
-11%
12.3
10.2
177.96
7.3
23.94
24.76
3%
3.1
20.2
196.66
7.3
39.50
41.72
6%
1
40.6
354.44
7.3
73.01
75.56
3%
12.3
10.2
361.96
21.8
20.84
24.08
16%
3.1
20.2
551.50
21.8
37.43
40.96
9%
1
40.6
1026.00
21.8
70.67
74.66
6%
12.3
10.2
863.82
65.3
18.87
22.05
17%
3.1
20.2
1500.77
65.3
34.94
38.72
11%
1
40.6
2911.74
65.3
67.77
71.99
6%
12.3
10.2
2373.08
196
16.95
15.94
-6%
3.1
20.2
4348.44
196
34.25
32.00
-7%
1
40.6
8492.86
196
65.68
64.02
-3%
This paper discusses some main indices in different sections of mining projects and analyse their
historical movements. Cost indices in the mining industry provide a general overview and a
mechanism for adjusting outdated cost information. One of the advantages of cost indices is to show
the effect of inflation in mining cost estimation. This study addresses the fluctuation of cost indices in
InfoMine from 1965 to 2008 and Marshal and Swift index from 1980 to 2009. As a result, all indices
over this period increased between six to ten times for coal mining and processing cost. While, the
operating labour index increased just 40 per cent over the last 40 years, in contrast with the oil and
natural gas fuel index which soared 22 and 51 times respectively from 1965 to 2008.
In addition, the operating cost in 20 mines in Australia and some mines in Canada and the US are
discussed. The availability of mine data in Australia is limited in comparison to US and Canada. The
CCG indices are used as proxy for estimating the cost of each item in Australian surface coal mining
cost. Then according to main variables and reliable data, an econometrics model is estimated to
calculate average operating cost.
The econometrics model for estimating operating cost focuses on deposit average thickness,
stripping ratio, capital cost and daily production rate as the independent variables and operating cost
as dependent variable. This model has been developed to explain the influence of independent
variables on estimating operating cost. The model shows that the capital cost and production rate has a
positive effect on estimated operating cost and deposit average thickness and strip ratio has a negative
effect on estimating operating cost. Estimated operating cost based on the model is compared by
operating cost calculated in InfoMine, and proves that the model estimation has ±20 per cent range of
accuracy with real data. Finally, the econometric model could be calculated for the future by
expanding indices for the future. Accordingly, accurate estimation for mining project cost is very
difficult; nevertheless these types of techniques could supply a view for one of the main uncertainty
variables in mining projects.
Australian Mining Technology Conference
27 - 28 October 2009
144
S SHAFIEE, M NEHRING and E TOPAL
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Australian Mining Technology Conference