APPLICATION OF MULTI-CRITERIA DECISION MAKING FOR GEOLOGICAL CARBON DIOXIDE STORAGE IN TURKEY

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*Corresponding Author: irem.firtina@eng.bau.edu.tr
Receiving Date: 20 September 2017 Publishing Date: 29 June 2018
Anadolu Üniversitesi Bilim ve Teknoloji Dergisi A- Uygulamalı Bilimler ve Mühendislik
Anadolu University Journal of Science and Technology A- Applied Sciences and Engineering
Year: 2018
Volume: 19
Number: 2
Page: 536 - 545
DOI: 10.18038/aubtda.339002
APPLICATION OF MULTI-CRITERIA DECISION MAKING FOR GEOLOGICAL
CARBON DIOXIDE STORAGE AREA IN TURKEY
İrem FIRTINA ERTİŞ *
Energy Systems Engineering Department, Faculty of Engineering and Natural Sciences, Bahçeşehir University,
İstanbul, Turkey
ABSTRACT
In contemporary era, carbon sequestration has become an important issue. Rapidly increasing population, higher life-
standards and technological advancements consistently increase the amount of emissions, especially CO2. Nowadays, the
most promising solution is to minimize the carbon dioxide emissions with using carbon capture and storage (CCS)
technologies for a better future. The problem is to choose the best location for CO2 storage which is a crucial and challenging
multicriteria decision problem. The objective of this paper is to determine the most appropriate city for carbon dioxide
storage in Turkey, by demonstrating a successful implementation of Multi-criteria Decision Making (MCDM) tool. This
study presents the use of MCDM method based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)
to assess the suitable location for CO2 storage. For that purpose, 4 alternative locations were evaluated via 8 criteria which
are determined according to the opinions of the experts with background information from the field. Towards this end,
MCDM method, namely, TOPSIS, was utilized for the location evaluation. Consequently, this method detects Diyarbakır as
CO2 storage area which is also one of the most important city of Turkey for having finished oil reservoirs and for its
geopolitical location.
Keywords: Carbon sequestration, Geological storage, Global warming, MCDM, TOPSIS
1. INTRODUCTION
The demand for natural resources and energy with the increase in population around the world is
gradually increasing. The world's population has increased by 2.5 times since 1950, and the energy
demand has increased seven-fold. Compared to the present, in 2030, it is expected to increase in a ratio
ranging from 40 to 50% of the energy consumption worldwide, and to increase higher than 100% of
this consumption in Turkey [1]
Considering the primary sources of energy, electric energy which is equivalent to 230 million barrels
of oil energy is consumed in the world every day. About 200 million barrels of electrical energy are
generated from fossil fuels. In the energy sector, petroleum, natural gas and coal are considered
together, with the hydrocarbon weighted [2]. Increase in the usage of fossil fuels such as coal,
petroleum and natural gas causes a major problem due to their destructive effects on the environment
in different sectors. This damage directly related with the greenhouse gas (GHG) emissions such as
carbon dioxide (CO2), NOx, SO2, etc. However, the most common greenhouse gas is CO2 due to the
high amount of emission because of the combustion of hydrocarbon fuels. Global warming potential
(GWP) was 35.3 Gt in 2014 because of carbon dioxide emission, there is a %55.6 increase only in the
last 13 years [1]. Atmospheric carbon dioxide concentration has increased 27% from 315.71 ppm in
1958 to 400.26 ppm in 2015 [3] and average global temperature increased by about 0.8°C since 1880 [4]
According to United States Environmental Protection Agency; in the next century, atmospheric carbon
dioxide concentration will be between 450-980 ppm and the temperatures will increase by 2.2-4.4°C
[2]. According to the World Energy Outlook Report, CO2 emission will increase 63% by 2030 from

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today’s level, which is 90% higher than the 1990 CO2 emission level. Thus, stronger actions/policies
are required and expected from the governments, including generation and utilization of certain
technology options to avoid massive CO2 emission increases [5]
There are many revolutionary designs to decrease the amount of carbon dioxide emissions, but they
are not feasible in most cases. The real question is how fossil fuels can be burned in more eco-friendly
way. Nowadays, the most promising solution is using carbon capture and storage (CCS) technologies
to minimize the carbon dioxide emissions for a better future. The whole process for CCS is to prevent
the carbon dioxide release to atmosphere from exhaust gas of the burned fossil fuels such like coal and
natural gas. CCS is a successful emission reduction option, which is used for capturing CO2 generated
from fuel use and preventing pollution by storing it. Besides energy supply security benefits, this
option has also numerous environmental, economic and social benefits; Blunt et al. (2010) [6]; Liao et
al. (2014) [7]; Kissinger et al. (2014) [8]. CCS can make large reductions in greenhouse gas emissions,
which involves capturing CO2 in deep geological formations [9].
There are four main carbon capturing ways; from industrial processes, from flue gases produced by
combustion of fossil fuels and biomass in air is referred to as post-combustion capture, from flue gases
produced by oxyfuel combustion with pure oxygen instead of air or, by a physical or chemical
absorption process, resulting in a hydrogen-rich fuel which can be used in many applications, such as
boilers, furnaces, gas turbines, engines and fuel cells, is referred to as pre-combustion capture.
After the capturing process, the greenhouse gas can be stored in underground reservoirs or stored in
ocean or stored as carbonated mineral form. Especially, geological storage of CO2 is the best way to
extinguish the emissions released to atmosphere by capturing CO2 from combustion chambers,
transporting it to the injection facility by using a pipeline and storing CO2 in geological formations. It
is estimated that 99 % of the CO2 will be injected and stayed for about 1000 years underground with
geological storage.
There are some previous studies proposing a variety of solution methods to find the optimum location
for CO2 storage in geological reservoirs. For example, Grataloup et al. (2009) focused on-site selection
for CO2 underground storage in deep saline aquifers [10]. Another study addressed different aspects
while considering potential CO2 storage reservoirs, including safety and economic feasibility of each
location [8]. Ramirez et al. (2010) studied a methodology to screen and rank Dutch reservoirs suitable
for long-term large-scale CO2 storage [11]. The screening was focused on gas, oil and aquifers fields.
Llamas and Cienfuegos (2012) presented a methodology for the selection of site areas for CO2
geological storage based on an analytic hierarchy process (AHP) [12]. Ertugrul and Karakasoglu
(2008) compared MCDM methods for facility location selection [13]. The proposed methods were
applied to a facility location selection problem of a textile company in Turkey. Kahraman et al. (2003)
studied four different fuzzy multi-attribute group decision making approaches, including fuzzy
modelling of group decisions and fuzzy analytic hierarchy process [14]. Although four approaches
have the same objective of selecting the best facility location, each has a different theoretic basis and
relate differently to the discipline of multi-attribute group decision-making.
The objective of this paper is to determine the most appropriate city for carbon dioxide storage in
Turkey, by demonstrating a successful implementation of MCDM tool. MCDM techniques are gaining
popularity in energy supply systems. MCDM techniques provide the means to solve such problems
supporting decision makers with the best option from a set of alternatives with respect to different
factors [15]. To evaluate the selected area for CO2 storage, a comprehensive analysis is developed
using some of the most prominently used MCDM technique, namely TOPSIS method. Based on the
results of the analysis, the most appropriate and the least desirable set of actions are determined for
CO2 storage. Although the results are specifically developed for cities of Turkey, the techniques that

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are used in this work might be inspiring for the other countries around the world. Like many other
countries in the world, the annual increase of CO2 emission in Turkey is quite high.
2. METHODOLOGY
In literature, Deveci, M., et al. (2015) made a comparison of five cities Adiyaman, Aksaray,
Diyarbakir, Afyon and Tekirdag in Turkey by choosing twelve criteria [16] Another studies from the
world; Grataloup et al. (2009) studied possible site location selection for Paris Basin for CO2
underground storage in deep saline aquifers [10]. Ramirez et al. (2010) gave a methodology to screen
and rank CO2 reservoirs suitable for storage application [11].
Our approach is like theirs in terms of involving several economic criteria (such as initial investment
cost, operation cost, transportation cost) rather than combining them all under the roof of a single
“economic” criterion. However, we find that their “benefit” criteria can be accounted for within the
“cost” criteria, thereby creating a single matrix of criteria which need to be minimized. Basically, cost
criteria and benefit criteria were distinguished in TOPSIS. Best solution for benefits criteria is close to
ideal positive cost criteria; while best solution for cost criteria, is close to ideal negative. TOPSIS is
developed for solving multiple decision-making problems by considering two reference points of the
ideal positive solution and the ideal negative solution simultaneously, best solution is the one that
close to the ideal positive solution and far away from the negative ideal solution.
2.1. Background information
Geological storage of CO2 is a way to eliminate the emissions released to atmosphere by capturing
CO2 from combustion chambers, transporting it to the injection facility by using a pipeline and storing
CO2 in geological formations. Three main geological storage areas are commonly used for CO2
storage. First one is injection into suitable depleted oil and gas reservoirs left from fuel production,
second one is saline aquifers that are unsuitable for using as water source and last one is injection to
un-minable coal and basalt deposits. But this study is only focused on suitable oil and gas reservoirs
for CO2 storage.
According to the number of wells some areas were considered to choose the best option for CO2
storage capacity (as seen in Table 1). The number of wells gives information about possible storage
zones and capacity of geological reservoirs which is related with criteria 1 (C1). Turkey has five oil
and natural gas well zones in which four of these places highly used for petroleum extracting and one is
for natural gas. Injecting CO2 into petroleum reservoirs is a better option to use in enhanced oil recovery.
Therefore, in this study, four areas were determined as potential storage zones as seen in Table 1.
Table 1. Number of wells in four areas [17]
Areas
Well numbers
Adıyaman
185
Diyarbakır
271
Batman-Siirt
209
Mardin-Şırnak
81
In this project, three closest plants were also considered for every storage zone. Less than 20MW
capacity plants were eliminated due to their low carbon dioxide amount (as seen in Table 2). Criteria 2
which is source proximity can be determined by using average distance to the storage zones.

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Table 2. Closest power plants to the storage zones
Storage Zones
Three Closest Power Plants
Distance to Storage Zone
Average distance (km)
Adıyaman
Afşin-Elbistan B Coal PP
126km
86
AKSA Şanlıurfa Natural Gas PP
74km
ODAŞ Şanlıurfa Natural Gas PP
57km
Diyarbakır
Mardin-1,2 Fuel Oil Plant
83km
110
AKSA Şanlıurfa Natural Gas PP
134km
ODAŞ Şanlıurfa Natural Gas PP
114km
Batman-Siirt
Mardin-1,2 Fuel Oil Plant
74km
71
İdil Fuel Oil Plant
54km
Silopi Coal PP
85km
Mardin-Şırnak
Mardin-1,2 Fuel Oil Plant
76km
55
İdil Fuel Oil Plant
19km
Silopi Coal PP
69km
Maximum electricity production in power plants directly related with CO2 emissions. Therefore,
comparison between the selected power plants depends on this parameter to clarify the maximum
supply from the plant to the atmosphere. The emission of CO2 per unit of electrical energy generated
is 900 g CO2/kWh from coal combustion and 400g CO2/kWh from natural gas combustion. Increased
use of natural gas is important to greenhouse gas reduction because natural gas emits about half the
amount of carbon dioxide than coal for the same energy produced (as seen in Table 3). Table 3 can be
considered to determine the maximum supply from the power plants which helps us to clarify the
criteria 3 and 4 (C3 and C4). Closest power plants which are given in Table 3, are only in operation
except the others in Table 2. So, we must take consider only these power plants in Table 3 for CO2
emissions to the atmosphere.
Table 3. Closest Power Plants with their capacity, used fuel, annual generation and CO2 release [18]
Closest Power Plants
Installed Capacity
(MW)
Used Fuel
Generation
(GWh/year)
Kg’s of CO2
release per kWh
Afşin-Elbistan B
1440
Coal
1430
0.9
Afşin-Elbistan A
1355
Coal
1029
0.9
Silopi
405
Asphaltene
2204
0.9
ODAŞ Şanlıurfa
140
Natural Gas
566
0.4
AKSA Şanlıurfa
147
Natural Gas
540
0.4
Geological topography of Turkey is very young and that creates constant changes in tectonic plates.
Tectonic activity is an important parameter to choose an appropriate injection site. Seismic zones map
of Turkey in Figure 1 published by the Ministry of Public Works and Settlement in 1996, which is also
approved by the Council of Ministers, and used geographic information system analysis to divide
Turkey into 4 regions as follows. On the seismic zones map of Turkey, first-degree seismic zone is
taken as region 1, second-degree seismic zone is taken as region 2, third-degree seismic zone is taken
as region 3, fourth-degree and fifth-degree seismic zones are taken as region 4 [19]. Adıyaman and
Diyarbakır are in region 2, Batman-Siirt region is in region 1-2 and Mardin-Şırnak is in region 2-3.
This map can be considered to determine the possible safety zones for CO2 storage and clarify criteria
5 (C5).
In literature, total cost for integrated CCS system in US$/ton based on the current technology. Capture
cost has the highest share for all power plants as seen in Table 4. Total cost per metric ton of CO2 is
the highest for new natural gas combined cycle plants. According to table 4 from the literature, experts
have decided to give high points to Adıyaman and Diyarbakır, low points to the other regions for
criteria 6 (C6).

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Table 4. Total cost for integrated CCS system [20]
US$/ton
Pulvarized Coal
Integrated gas combined
cycle (IGCC)
Natural gas combined
cycle (NGCC)
CO2 capture cost
65.9
52.3
214
Transportation Cost
3.5
3.5
3.5
Storage Cost
3.2
3.2
3.2
Total Cost
72.6
59
220.7
Figure 1. Seismic zones map of Turkey [19]
According to the socio-economic development in South Eastern Anatolia region, the order of cities is
Diyarbakır > Adıyaman > Batman-Siirt > Mardin-Şırnak. Therefore, this information gives an idea
about transportation availability (C7) and infrastructure availability criteria (C8) [21].
2.2. Case Study
This study presents a model using a method for selecting candidate sites for underground CO2
geological storage in Turkey. A committee of decision makers (D1, D2, D3 and D4) with background
information was formed to select the best alternative using 8 criteria as provided in Table 4. Four
alternative locations for depleted reservoirs (depleted oil and gas reservoirs) are determined by using
background information about different locations in Turkey: Adıyaman, Diyarbakır, Mardin-Şırnak
and Batman-Siirt. They were evaluated by MCDM method named TOPSIS. Structure of CO2 storage
area selection with MCDM method is shown in Figure 2.
In summary, the evaluation criteria that are selected in our model are “Cost”, “Transportation
availability”, “Infrastructure availability”, “Regional risks”, “Environmental contribution”, “Storage
capacity” “Source proximity”, “Maximum Supply” which has a more detailed version of the general
sets of technical, environmental/social and economic criteria as seen in Table 5.

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Figure 2. Best location selection for CO2 storage with MCDM method
Table 5. Main and sub-criteria of carbon dioxide storage in geological reservoir with their type and definitions
Main Criteria
Sub-Criteria
Definition
Criteria Type
Technical
Storage capacity
(C1)
The capacity of the underground geological
formations (number of wells)
Benefit
Source proximity
(C2)
Distance to thermal power plants (km to CO2
resources)
Benefit
Maximum Supply
(C3)
kg CO2 /kWh (from powerplants)
Benefit
Environmental
/Social
Environmental
contribution (C4)
Social (human health) and environmental
contributions.
Benefit
Regional risks
(C5)
Risks in the region (like earthquake risk,
natural risk, etc.)
Cost
Economical
Cost (C6)
Initial for the investment, maintenance cost,
transportation cost
Cost
Transportation
availability (C7)
Quality of transportation and distribution
infrastructure.
Benefit
Infrastructure
availability (C8)
Technological quality and availability of basic
infrastructure, pressure and flow systems.
Benefit
3. RESULTS AND DISCUSSION
The assessments for the eight criteria are determined by experts from the field with background
information which also evaluate the four alternatives (locations) for each of the 8 criteria. This study
presents a model using a method for selecting candidate sites for underground CO2 geological storage
in Turkey. The next step is to determine the best storage area according to the selected criteria and
form a “decision matrix” to be used in TOPSIS method. To this end, again a combination of expert
opinions and the relevant literature is used, and a “criteria vs methods” matrix is formed as in Table 5
initially. A scale of 1 to 10 is used, where one corresponds to the situation where the method has little
impact on the criterion, while 10 corresponds to the situation where the method has high impact
on/high relation with the criterion. The decision matrix is then formed using Table 5, the storage areas
in different cities and the point score of each city according to each criterion.
MCDM method for CO2
storage
C1 C2 C3 C4 C8C5 C7C6
A1: Adıyaman A2: Diyarbakır A3: Batman-Siirt A4: Mardin-Şırnak
D1
D2
D3
D4

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Four alternative locations for depleted reservoirs (depleted oil and gas reservoirs, aquifer reservoirs,
salt cavern reservoirs, coal mine and mined cavern) are determined by experts: Adiyaman (A1),
Diyarbakir (A2), Mardin-Şırnak (A3) and Batman-Siirt (A4). The TOPSIS method starts with the
decision matrix in Table 6. Next, a standard decision matrix is computed by using the elements of
decision matrix, and the weighted standard decision matrix is developed by multiplying the values in
the standard decision matrix with the weight value of that criterion. Since [22] suggests assigning
equal weights to each evaluation criterion to reduce social conflicts and increase fairness, we used
equal weights for each criterion.
Table 6. Decision matrix
Alternatives
C1
C2
C3
C4
C5
C6
C7
C8
Adıyaman
(A1)
185
86
10
7
8
8
9
7
Diyarbakır (A2)
271
110
8
7
9
10
7
8
Batman-Siirt
(A3)
209
71
6
9
7
7
6
6
Mardin-Şırnak
(A4)
81
55
6
5
6
5
5
6
Table 7. Normalized decision Matrix
Alternatives
C1
C2
C3
C4
C5
C6
C7
C8
A1
0.46555
0.51802
0.65094
0.49010
0.52750
0.51856
0.65122
0.51465
A2
0.68197
0.66258
0.52076
0.49010
0.59344
0.64820
0.50650
0.58817
A3
0.52595
0.42766
0.39057
0.63013
0.46157
0.45374
0.43414
0.44113
A4
0.20384
0.33129
0.39057
0.35007
0.39563
0.32410
0.36179
0.44113
TOPSIS method is conducted; a normalized decision matrix and a weighted normalized decision
matrix are developed based on the decision matrix presented in Table 6.
The next step in TOPSIS methodology is to compute the ideal (
*
A
) and the negative ideal (

A
)
solutions. TOPSIS method assumes that each evaluation criterion has an increasing or decreasing
trend. That is, in a cost-minimization problem as ours, to form the positive ideal solution set (negative
ideal solution set), the minimum (maximum) value in each column of the weighted standard decision
matrix should be selected. The normalized and weighted normalized decision matrix of the 8 criteria
are presented in Table 7 and Table 8, respectively.
Table 8. Weighted normalized decision matrix
Alternatives
C1
C2
C3
C4
C5
C6
C7
C8
A1
0.05819
0.06475
0.08137
0.06126
0.06594
0.06482
0.08140
0.06433
A2
0.08525
0.08282
0.06509
0.06126
0.07418
0.08103
0.06331
0.07352
A3
0.06574
0.05346
0.04882
0.07877
0.05770
0.05672
0.05427
0.05514
A4
0.02548
0.04141
0.04882
0.04376
0.04945
0.04051
0.04522
0.05514

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Finally, the alternatives are evaluated using TOPSIS method. TOPSIS is developed by Yoon and
Hwang in 1980 [23] as an alternative to ELECTRE method and based on the principle of proximity of
decision points to the ideal solution. Ideal solution is the best performance on each criterion; however,
in general different solutions produce the ideal solution under each criterion, rendering to reach the
ideal solution by selecting a single decision alternative impossible. Therefore, the decision maker
proceeds with selecting the closest alternative to the ideal solution. Towards that end, the vector of
“Positive Ideal Solution (PIS)”, which maximizes profit criterion and the vector of “Negative Ideal
Solution (NIS)” which maximizes cost criterion are developed. According to TOPSIS, best alternative
should be closest to PIS, and farthest from the NIS [24] The PIS and NIS that are developed based on the
previous steps are shown in Table 8. The positive ideal solution (PIS) set and the negative ideal solution
(NIS) set that are developed based on the previous steps of the TOPSIS algorithm are shown in Table 9.
Table 9: PIS and NIS values
Criteria
PIS
NIS
Storage capacity
0.085246634
0.025479621
Source proximity
0.065817217
0.03949033
Maximum Supply
0.083451858
0.050071115
Regional risks
0.024592415
0.073777246
Cost
0.058114889
0.087172334
Environmental contribution
0.076497537
0.045898522
Transportation availability
0.069904665
0.038835925
Infrastructure availability
0.070803205
0.044252003
Next, the deviations of each decision point from the ideal and negative ideal solutions are computed
via using Euclidian Distance Approach; and the relative proximity of each decision alternative to the
ideal solution (
*
i
C
) is computed using the ideal and negative ideal separation measures. Here,
*
i
C
takes a value between 0 and 1; and the closer
*
i
C
value to 1 is, the closer the decision alternative is to
the ideal solution; whereas the closer
*
i
C
value to 0 is, the closer is the decision alternative to the ideal
solution [25]. Applying the appropriate formulas, the values of
*
i
S
,

i
S
and
*
i
C
; the result regarding
the preferences of the alternatives are presented in Table 10.
Table 10: Final Computations and Ranking According to TOPSIS
Alternatives
Positive Ideal
Discrimination
Measures (
*
i
S
)
Negative Ideal
Discrimination
Measures (

i
S
)
Closeness to Ideal
Solution (
*
i
C
)
Ranking
A1
0.04453703
0.070942854
0.614330838
2
A2
0.03885668
0.090357772
0.699285365
1
A3
0.07252811
0.048810603
0.402267344
3
A4
0.09815458
0.042859109
0.303935807
4
The ranking of alternatives obtained from fuzzy TOPSIS is A2> A1 > A3 > A4. Closeness coefficient
is used as a basis for determining the ranking order for TOPSIS. We can conclude that A2 alternative
is the best location for CO2 storage; on the other hand, A1, A3 and A4 are less suitable locations than
A2 alternative.

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4. CONCLUSION
This study presents the use of MCDM method based on TOPSIS to assess the suitable location for
CO2 storage. A case study from Turkey is illustrated for evaluating the results of the proposed area by
this method. The method can give successful results for CO2 location selection. This method detects
A2 (Diyarbakır) as the best alternative for CO2 storage location in Turkey based on the set of criteria.
Diyarbakır selected area is also one of the most important cities of Turkey for having finished oil
reservoirs and for its geopolitical location.
The main aim of this study was to investigate how TOPSIS can be utilized to solve the facility location
selection problem for CO2 storage. The proposed solutions based on the determined set of criteria are
general and reusable; hence, it can be applied to the same problem in other countries than Turkey. We
also show how these regions can be evaluated from all perspectives including economical, technical,
environmental and social, by mathematical MCDM techniques. It is important to keep in mind that the
other multi criteria decision methods (PROMETHEE I and II, etc.) and/or their combinations can also
be used as effective solutions to the location selection problems. One limitation of the method
described in this paper is the fact that MCDM methods depend heavily on expert opinion; as the
weights attributed to each criterion play an important role in the result. Therefore, these solution
procedures need a complimentary sensitivity analysis, which does not exist in MCDM by their nature.
One way to handle this problem could be using mathematical programming procedures and
conducting a sensitivity analysis to see how robust the results are for different ranges of parameter
values. Our future research agenda includes this kind of an analysis procedure.
ACKNOWLEDGEMENTS
This research did not receive any form of grant from funding agencies in the public, commercial, or
not-for-profit sectors.
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