Developing a prototype for determining alternative sources of natural gas supply.
Alijani, Ghasem S. ; Kwun, Obyung ; Omar, Adnan 等
INTRODUCTION
Despite its rapid growth in recent years, Liquefied natural gas
(LNG) remains a relatively small contributor to world gas demand (under
7% of the total world gas demand in 2005) and even to total
internationally traded gas, (about 22% of gas trade) according to the
National Petroleum Council (2007). Pipeline gas still dominates
international trade most notably supply to Western Europe from Russia,
North Africa and Norway and supply to the US from Canada. With regards
to regional LNG trade, the Pacific Basin and Asian markets almost double
the size of the Atlantic Basin and Mediterranean markets.
By end of 2010, LNG trade is expected to be more than 10 trillion
cubic feet (tcf) annually from the recent 6.5 tcf, with the United
States expecting most demand followed by Northern Europe, Japan, South
Korea, China and India (BP, 2005). Although trade movement is lower in
the Pacific, countries in this region supplied 59% of the global LNG
market. In 2006 and 2007 LNG shipment rose by 11.8% and 7.3%
respectively; which is in line with historical average considering
increased shipments from Qatar and Nigeria (Riihl 2007 & BP 2008).
Asia, recorded an incremental average of 10% in LNG imports with Japan
and South Korea being the major importing nations (PRLog, 2007), while
European imports rose by 20%. In 1995, there were eight LNG exporting
countries and nine importing countries (Ogj, 2007). By 2007 the number
has increased to 15 exporting countries and 17 importing countries.
World trade in LNG reached a total of 211.1billion cubic meters (bcm) in
2006, an increase of 11.7% on figures for the previous year, according
to Cedigaz (2008).
In 2002 only 23% of world gas consumption was imported and 26% of
that was in the form of LNG (Jensen et al., 2004). Between 2000 and 2020
world demand is forecasted to grow by 1727bcm (IEA, 2002). In the same
light the US energy information administration also predicts a similar
growth of 54tcf between 2005 and 2030 (EIA, 2008). With the exception of
Russia and other countries of Eurasia, natural gas production is
expected to represent a significant portion for exports in the Mideast
(Qatar) and Africa (Nigeria, Algeria, Egypt and Libya).
STATEMENT OF THE PROBLEM
The evolution of natural gas trade between Eurasia and its western
neighbors cannot be cited without upheavals. In the past, gas importing
countries feared an interruption in important gas supplies for a variety
of reasons such as contract disputes between Algeria and its customers
(Hayes, 2006), political unrest in Indonesia (von der Mehden &
Lewis, 2006) and transit country risk such as in Ukraine and Belarus for
Russian exports (Victor & Victor, 2006). In March 2008 disputes
between Russia and Ukraine accompanied a reduction of Russian supply for
3 days, and Turkmenistan cut supplies to Iran citing technical issues
with the pipeline and a breach of pricing contract (EIA, 2008).
According to Stratfor (2008), Turkmenistan shut natural gas supplies to
Iran (which holds the world's second largest natural gas reserves)
at the start of 2008 due to pricing squabbles between the two countries.
STATEMENT OF THE OBJECTIVE
The objective of this project is to investigate the present status
and trends of natural gas supply and develop a prototype to accommodate
planning and implementation by providing the following capabilities: (i)
provision of alternative efficient natural gas distribution routes in
terms of minimum cost and risk, (ii) identification of the alternative
natural gas supply sources given a scenario (supply crisis), and (iii)
assess the influence of stakeholders in the selection of alternative
sources of natural gas supply. This paper focuses on the exposition of
the prototype components and its features while special emphasis is
placed on the contribution of this system in providing integrated
solutions to the natural gas supply source problem. The proposed model
thus identifies alternative efficient gas supply sources in terms of
cost and risk. The Model layer comprises the database that facilitates
the decision support system and provides tools to observe time series
data, with a linkage to real time data acquisition and monitoring
(Ramachandra et al., 2005).
BACKGROUND
The development of a decision support system (DDS) and applications
to provide solutions to problems in natural gas management and logistics
has attracted substantial research efforts in the past two decades.
Particular examples include the use of analytical hierarchy process as a
decision support system in the petroleum pipeline industry (Nataraj,
2005) and applying GIS to provide alternative routes (Kirchner, 2007).
Chin & Vollman (1992), describe a methodological framework for
developing a decision support model for natural gas dispatch. Queiroz et
al., (2007) describe a DSS model to aid designers in the task of
elaborating distribution network projects by using optimization and
artificial intelligence.
Zografos & Androutsopoulos (2008) proposed a DSS to accommodate
the hazardous materials risk management process by integrating vehicle
routing and emergency response planning decisions. In particular, they
argue that a decision support system for hazardous materials
transportation risk management should address the following issues: (i)
cost-risk trade off of alternative hazardous materials distribution
routes, (ii) Optimum deployment and routing of the emergency response
units, and (iii) Optimum evacuation plans.
Ramachandra et al., (2005) introduced a decision support system for
regional domestic energy planning. In particular, they argue that a
decision support for domestic energy planning should address the
following issues: (i) determine fuel consumption patterns in various
agro- climatic zones, (ii) provide means for entering, assessing and
generating reports and (iii) analyze energy indices and interpretation
for sound decision making. They developed a prototype that could
transform data into information and help decisions for domestic energy
consumption to assess bioenergy potential for Kolar district (Karnataka
state, India) using Bioenergy Potential Assessment (BEPA), a spatial
decision support system.
Yildirim & Yomralioglu (2007) have developed an interactive
GIS-based Pipeline Route Selection by ArcGIS in Turkey. They integrated
GIS technology into the decision support system to provide alternative
routes and calculate construction and operation cost. Giglio et al.
(2004) developed a decision support system for real time risk assessment
of hazardous material transport on road. They focused their study on the
risks associated with hazmat road transport by tanker trucks of
petroleum products.
Considering the practical issues, requirements, and circumstances
involved in natural gas distribution networks, a Decision Support System
(Sprague & Watson, 1989) model becomes suitable and appropriate to
the problem approached in this project. The prototype has been developed
along the lines of Decision-Support System Workbench for Sustainable
Water Management Problems introduced by Morley et al, (2004) and optimal
routing of natural gas developed by SINTEF.
METHODOLOGY
As stated earlier, this research focuses on developing a prototype
to study the flow of natural gas supply between consumer (importer) and
producer (exporter) countries. Further, the system identifies the degree
of interactions among the countries through the use of certainty and
dependency factors. If the relationships among the export and import
countries are ever disturbed, these factors, along with data on major
suppliers and importers, help to determine the alternatives in the in
the natural gas distribution chain.
DATA COLLECTION
As major players in the natural gas sector, forty nine countries
were selected. Data on these countries was obtained from the Central
Intelligence Agency's 2008 publication of World Fact book, and the
BP's 2008 Statistical Review of World Energy. Since data was
derived from two sources, comparison was made to ensure compatibility
between the figures. The data was tabulated using reserve (R),
consumption (C), production (P), export (E), and import (I) variables.
Net reserve (Nr) was calculated using Nr = R - C + I - E.
The selected countries (49) are divided into three categories;
Exporting, Self-sufficient, and Importer groups. Twenty eight of them
were selected with selection being based on the total volume of natural
gas each country holds in that category. This volume ranges from largest
to smallest with only the major players (countries that carry high
volumes of import or export) categorized as shown in the following
table. Aside from political and technical factors, the greater the
volume, the greater the influence and role it exerts in natural gas
trade flow.
After the data was classified into the three categories, emphasis
was placed on the relationship between the major suppliers and importers
in terms of quantity of gas distributed, as well as the relational
factors that govern the flow of natural gas between these countries
(Figure1).
The directions of the arrows indicate the actual direction of flow
of natural gas from the suppliers to importers. Based on the number of
suppliers destined to each importing country, a certainty factor was
attributed to each receiving country indicating the degree of assurance
for natural gas supply for the importer. The higher the certainty
factor, the higher the supply assurance and vice versa. On the exporting
side, a Dependency factor was defined to determine how many major
importer countries depend on each of the exporting countries.
[FIGURE 1 OMITTED]
Apart from dependency and certainty factors, the relationships (Rs)
among the export and import countrie are influenced by several other
factors including Political (Pf), Production cost (Pc), Transport cost
(Tc), proved Reserves (Rp), and volume of Production (Pr). Thus, a more
comprehensive relationship can be expressed as: Rs = <[P.sub.f], Pc,
Tc, Rp, Pr>.
In determining the degree of strength of the supply relationship,
within the framework of this model, the factors were rated on a scale of
0 to 3, with 3 having a stronger influence on the relationship and a
factor with 0 having a weaker influence. In addition the following
assumptions were made:
Table 2: Relationship guiding factors between importing and
exporting countries
Exporter
Importer Russia Canada Norway Algeria Netherlands
United states PF=3 PF=2
R=3 R=2
Pc=3 Pc=2
Tc=3 Tc=1
Pr=3 Pr=1
Japan PF =2
R=1
Pc=1
Tc=1
Pr=3
Germany PF =3 PF =3 PF =3
R=3 R=1 R=0
Pc= 0 Pc= 2 Pc = 2
Tc= 3 Tc = 3 Tc = 3
Pr=3 Pr=3 Pr=3
Italy PF =3 PF =3 PF=3 PF =3
R=3 R=1 R=2 R=1
Pc= 0 Pc = 1 Pc= 3 Pc= 1
Tc= 3 Tc= 3 Tc= 3 Tc= 3
Pr=3 Pr=2 Pr=2 Pr=2
France PF =3 PF =3 PF =3 PF =3
R=3 R=3 R=3 R=2
Pc= 1 Pc= 2 Pc= 3 Pc= 2
Tc= 3 Tc= 3 Tc= 3 Tc = 3
Pr=3 Pr=3 Pr=3 Pr=3
South Korea PF =2
R=3
Pc= 2
Tc= 2
Pr=3
United PF =3 PF =3 PF =3
Kingdom R=2 R=3 R=2
Pc = 2 Pc= 3 Pc=2
Tc = 3 Tc= 2 Tc = 3
Pr=3 Pr=3 Pr=3
Mexico
Turkey PF =3 PF =3
R=3 R=2
Pc =1 Pc =2
Tc =3 Tc= 3
Pr=3 Pr=3
Exporter
Importer Qatar Indonesia Malaysia Nigeria Trin. &
Tobago
United states PF =3 PF =3, PF =3
R=3 R=3 R=3
Pc=3 Pc=1 Pc=3
Tc=2 Tc=2 Tc=3
Pr=2 Pr=1 Pr=3
Japan PF =2 PF =3 PF =3 PF =3 PF =1
R=2 R=3 R=3 R=2 R=0
Pc=1 Pc=2 Pc=2 Pc=1 Pc=1
Tc=2 Tc=3 Tc=3 Tc=1 Tc=1
Pr=2 Pr=3 Pr=3 Pr=1 Pr=1
Germany
Italy
France PF =2 PF =2
R=3 R=1
Pc= 3 Pc= 2
Tc= 2 Tc= 1
Pr=2 Pr=2
South Korea PF =3 PF =3 PF =3 PF =3 PF =2
R=3 R=2 R=2 R=3 R=1
Pc= 3 Pc= 1 Pc= 1 Pc= 2 Pc= 1
Tc= 2 Tc= 3 Tc = 3 Tc= 1 Tc= 1
Pr=3 Pr=3 Pr=3 Pr=2 Pr=2
United PF =3 PF =2
Kingdom R=3 R=1
Pc =2 Pc =1
Tc =2 Tc =1
Pr=3 Pr=1
Mexico PF =2 PF =3
R=3 R=2
Pc =3 Pc =2
Tc =1 Tc = 3
Pr=2 Pr=2
Turkey PF =1 PF =1
R=2 R=1
Pc =3 Pc =1
Tc =2 Tc =1
Pr=1 Pr=2
PF = Political Factor, Pc= Production cost Tc= Transport cost,
R= Reserve, Pr= Production level. The empty cells represent no
relationship between the two countries that it intersects
Political relations set the pace for other factors to come in
especially the economic factor that includes all costs, and production.
Reserves (Rp) and Production (Pr) factor ratings are determined by
the exporting country's rank order and political influence of the
country it supplies natural gas to. Transport cost (Tc) rating is
determined by distance. The greater the distance the higher the
transport cost and the lower the influence on the strength of
relationship.
In the light of the above discussions, the relationships among the
importers and exporters can be quantified as shown in Table 2.
TESTS AND DISCUSSION
The relationship between countries in the natural gas supply chain
is a result of not only the quantity of gas that flows between these
countries but also other factors providing a long term
certainty/uncertainty of supply to importers. In the event of a dispute
that may arise from, for example, the breach of pricing contract (viz
Russia and Ukraine in 2008), the relationship becomes fragile,
jeopardized by the shutdown of natural gas supply which may lead to the
termination of the long term relationship. This creates a "what if
scenario and forces importing countries to seek ways to minimize the
consequences from such a situation. These consequences can only be
mitigated through a prototype represented by a database which through
the process of data mining will enhance the formulation and
implementation of a solution as a decision support tool for the planning
and organization of the natural gas value chain, helping to decide who
will be the best alternative supplier.
In developing the system we followed the classical five stage
project life cycle i.e. user requirements identification, functional
specifications, system design, prototype development and evaluation. The
user requirements will be achieved based on the following: Determination
of alternative routes for the supply of natural gas based on cost and
risk minimization;
Determination of alternate supplier;
Computation of scenario probabilities and measure of expected
consequences (risk assessment);
Performing an analysis on the number of suppliers available to
achieve alternative service for unforeseen scenarios.
DATABASE
As stated, this paper focuses on the development of a prototype to
study the effects of different natural gas supply and demand scenarios
and provides alternative solutions to the problem of distribution in a
cost effective manner and at minimal risk. In order to validate the
approach and the characteristics of the prototype, a relational database
is designed to analyze and monitor the flow of supplies. This database
includes Supplier, Importer, Tanker Truck, and Supply Route. The
Supplier table consists of attributes for the name of exporting
countries, their regional location and attributes for the factors that
may determine the strength of the exporting country in an
importer-exporter relationship. Similar attributes are found in the
Importer table. The Tanker Truck table contains attributes like the
tanker or truck name, their capacity, and commission date. The Supply
Route table merely describes the route information (maritime or land)
with attributes for distance, route, tanker or truck name that ply the
route and tanker, or truck ID for referencing. All the tables have
primary key attributes which in our case are auto generated numbers for
referential integrity.
In order to reinforce the rule for referential integrity, as well
as reduce inefficiency errors, output errors, and redundancies that may
lead to data anomalies in our database, a Many-to- Many relationship was
implemented between the supplier and importer tables using a Bridge or
Composite entity (Tanker Truck table). To create this entity, the
primary keys of the supplier and importer tables were included into the
entity table. The essence of this relationship reinforces the idea that
one country can export natural gas to many countries, and at the same
time a country can import natural gas from many other countries.
Meanwhile a One-to-Many relationship was implemented between the entity
table and the supply route table, since a tanker or truck can ply many
routes based on cost effectiveness and risk minimization.
As earlier mentioned, the executive decision making process to
select an alternate supplier arises based on an unprecedented scenario
that may lead to the suppression or total cut of natural gas supply,
like the case between Nigeria and the US due to frequent attack by armed
groups in the Nigerian oil rigs, contract disputes between Algeria and
its customers, the 2006 transit country risk such as Ukraine and Belarus
for Russian exports, pricing squabbles between Turkmenistan and Iran,
and the March 2008 disputes between Russia and Ukraine. The outcome of a
similar scenario was represented by highlighting and deleting a record
from the Supplier table. In our approach the relationship between France
and Algeria was elaborated upon as shown in Figure 2
The above scenario was supposedly accompanied by the shutdown of
natural gas (LNG) supply from Algeria to France, putting France in a
critical situation since more than half of its supplies come from
Algeria. As a solution to this problem, stakeholders involved in the
Algeria- France gas distribution chain will have to make a query from
the database of suppliers as shown in Figure 3 in order to get the best
alternative supplier in terms of risk and cost minimization.
Doing this query requires stakeholders to determine the best
criteria to be used. Since the sum of factors in each relationship
determines the strength of that relationship, the sum of the values of
factors was decided to be the best criteria in our case; the higher the
sum of factors the stronger the strength of the relationship and vice
versa. Since each factor is weighted on a 3 point scale, the maximum sum
a relationship could attain is 15 based on our 5 factors. Consequently
the criteria ">=13" was defined for the Supvaluesum
attribute to be the best criteria. From the query in Figure 3, the best
alternative was decided upon based on the query results as shown in
Figure 4.
[FIGURE 2 OMITTED]
[FIGURE 3 OMITTED]
[FIGURE 4 OMITTED]
Following our database query results in Figure 4, Norway, with 14
as the sum of values of factors, represents the best alternative for
France to import natural gas. However, Russia and the Netherlands also
present options for alternate supplies since they fall within the range
of our query criteria. The decision for Norway was based on the fact
that it had the highest sum of values of each factor. This could be
difficult to implement in real world situation, for the simple reason
that the most influential factors in a relationship (political and
economic) could be lowly weighted over other factors, making the
decision for an alternate supplier faulty and misrepresented. Also in
the event where the query results are identical-- i.e., the same sum of
values of factors and the best alternative; Norway in our case happens
to present another difficult scenario to France, the decision to
separate the Netherlands and Russia will no more be based on sum of
values of factors since they have the same values, but on the rank order
of the supplier in terms of global natural gas production. With this in
mind, another query is conducted with supplier rank order as our new
criteria which is defined as"<4" as shown in Figure 5.
[FIGURE 5 OMITTED]
Based on rank order, the best alternative will be determined by
position of that supplier in the rank classification of suppliers. From
our query results, the supplier with the smallest rank order in this
case will present the best conditions and opportunities to be considered
an optimum alternative. Therefore Russia, with a supplier rank order of
1 will become the next best alternative to France for natural gas supply
after Norway, as shown in Figure 6.
[FIGURE 6 OMITTED]
Distance represented by transport cost (Tc), economics represented
by production cost (Pc), and political factors are very influential in
determining the strength of a supplier-importer relationship.
Countries with a high certainty factor (Japan, United States,
France and South Korea) are the most dependent on natural gas supply,
while those with a low certainty factor (Germany, Mexico, Italy) have
the most critical situation. But this dependency does not equal
increasing volumes of natural gas that is moved between the two
countries since one country can supply more than half the volume of
natural gas than any other country in that chain of relationship. This
can be exemplified by Russia. According to the BP Statistical review of
world energy 2008, The Russian Federation supplied 35.33 bcm of natural
gas to Germany, a volume that exceeds supplies from the UK and Norway
(26.64bcm) or the UK and the Netherlands (22.03bcm) to Germany.
In this study we found that the proportion of natural gas reserves
in a country significantly influences its position as either an exporter
or an importer as shown in Table 1. However, contrary to our
predictions, the United States exports natural gas to Japan and Canada.
The reason for this classification may be due barely to the fact that
its actual proven reserves equal its net reserves (NR) plus additional
imports. Additional imports are then used for export (though a very
small volume compared to actual US imports) to Canada and Japan, but
unfortunately the US still remains a major importer and consumer of
natural gas in the world. In addition the frequency of error was not
significant, due to the smaller number of data (2007) that was treated.
The determination of the weight of each factor in an
importer-exporter relationship is an outcome of the strength of
political and economic factors. Consequently the outcome in this study
with the exception of our query results could be generalized with the
results representing real world situation.
CONCLUSION AND RECOMMENDATIONS
The overall problem of determining alternative sources of natural
gas supply is a complex interdisciplinary problem that should be faced
with many view points, one of which is optimal risk-based planning of
natural gas routing. This paper presented an integrated multi-scaled
prototype for the selection of an alternative supplier of natural gas.
The prototype aimed at computing dynamic risk scenario in real-time
natural gas supply and it was based on a methodology that determined the
critical factors that support the relationship between two or more
countries in the natural gas supply chain. It provides a user friendly,
model-based environment for determining the best alternate supplier and
it establishes supplier-importer relationships while evaluating the
factors that govern these relationships. A major feature of the
prototype is that it integrates framework and mathematical models,
including a relational database that was used to exemplify stakeholder
decision making procedures to determine the best natural gas supplier
given an unprecedented scenario between France and Algeria.
Once the decision support prototype is verified on historical and
real-time data it should be able to be extended to link with or enhance
current scenario planning systems of natural gas supply. However the
prototype is general purpose, in the context that it needs to be adapted
to suit the scenario environment and conditions it will be applied in.
Recommendations for future studies are both technological and
methodological. A technological aspect would be related to the
enhancement of information to monitor and control factors that may lead
to a possible outcome of a scenario. Methodological aspects could deal
with the calibration of the model on a set of historical data and on
practical experience of fleet (tanker or truck) and with the integration
with scenario planning modules. In addition, further research is
required in identifying the input factors of each scenario and
undertaking sensitivity analysis. Also, a quantitative comparative
analysis of the results of each scenario could be finalized.
REFERENCES
BP (2005). Statistical Review of World Energy 2005. Putting Energy
in the spotlight, pp 20-29. Retrieved on 2/7/2009 from
http://www.bp.com/liveassets/
bp_internet/switzerland/corporate_switzerland/STAGING/
local_assets/downloads_pdfs/s/statistical_review_of_world_energy_2005.pdf
BP (2008). Statistical review of world energy 2008. pp 22-31.
Retrieved on 2/17/2009 from
http://www.bp.com/liveassets/bp_internet/globalbp/
globalbp_uk_english/reports_and_publications/statistic
al_energy_review_2008/STAGING/local_assets/downloads/
pdf/statistical_review_of_world_energy_full_review_2008.pdf
Cedigaz press release (2008). The 2007 Natural gas year in review.
Retrieved on 2/15/2009 from
http://www.cedigaz.org/news_statistics/statistics.htm
Central Intelligence Agency (2008).The CIA World Factbook 2008.
Retrieved on 3/3/2009 from
https://www.cia.gov/library/publications/the-world-
factbook/rankorder/2179rank.html
Chin, L. & Vollman, T. E. (1992). Decision support models for
natural gas dispatch. Transportation Journal; Winter 1992, Vol. 32 Issue
2, p38-45, 8p.
Energy Information Administration. International Energy Outlook
2008. Retrieved on 3/3/2009 from
http://www.eia.doe.gov/emeu/international/reserves.html
Giglio, D., Minciardi, R., Pizzorni, D., Rudari, R., Sacile, R.,
Tomasoni, A & Trasforini, E. (2003). Towards a decision support
system for real time risk assessment of hazardous material transport on
road, Proceedings of the International Environmental and Monitoring
Software Society 2004 pp 1-6.
Hayes, M.H., Victor, D.G., & Jaffe, A.M. (2006). "The
Transmed and Maghreb projects: gas to Europe from North Africa,"
Natural Gas and Geopolitics: From 1970 to 2040, chapter 3, Cambridge
2006.
International Energy Agency. World energy outlook 2002. Retrieved
on 2/23/2009 from http://www.iea.org/textbase/nppdf/free/2000/weo2002.pdf
Jensen, E. L., Bernhardt, K. S. & Huaco, D. R. (2004). Decision
support for slope construction and repair activities: An asset
management building block. MTC Project 2000-03.
Kirchner, K. GIS based decision support for gas pipe maintenance.
Retrieved on 2/5/2009 from http://www.gisdevelopment.net/application/
miscellaneous/mis001_1.htm
Morley, M.S., Makropoulos, C.K., Savic, D.A. & Butler, D.
(2004): Decision- Support System Workbench for sustainable water
management problems. In Pahl-Wostl C, Schmidt S, Jakeman A, editors.
International environmental modeling and software society iEMSs 2004
"Complex and integrated resources management". Germany:
Osnabrueck; 2004.
Nataraj, S. (2005). Analytical hierarchy process as a decision
support system in the petroleum pipeline industry. Issues in information
systems, vol VI, No. 2.
National Petroleum Council (2007). Liquefied Natural Gas. Working
Document of the NPC Global Oil& Gas Study. Retrieved on 2/15/2009
from http://www.npc.org/Study_Topic_Papers/13-STG-LiquefiedNaturalGas.pdf
PRLog: Global LNG Market--The Road ahead (2007). Retrieved on
1/29/2009 from http://www.prlog.org/10031711-
global-lng-market-the-road-ahead.html
Queiroz L. M. O, Cavellucci C., Gonzalez J. F. V., Garcia V. J.,
Lyra C., Franca P. M.& Almeida J. (2007). Decision support system to
design distribution networks. 19th International Conference on
Electricity Distribution Vienna 2007.
Ramachandra, T.V., Krishna, S. V. & Shruthi, B.V. (2005).
Decision support system for regional domestic energy planning. Journal
of scientific & industrial research Volume 64, March 2005, pp
163-174.
Ruhl, C. (2007). BP Statistical review of world energy 2007. pp
11-12. Retrieved on 2/17/2009 from
http://www.bp.com/liveassets/bp_internet/bp_korea/bp_korea_korean/
STAGING/local_assets/downloads_pdfs/b/
bp_Statistical_Review_of_World_Energy_
report_2007_christof_ruhl_speech_and_slides.pdf
Su, K.W.& Huang, S.L. (2006). Developing a usable mobile expert
support system for emergency response center. Proceedings of the
International MultiConference of Engineers and Computer Scientists
(IMECS) 2006 pp 13-17.
Victor & Victor (2006). "Bypassing Ukraine," Natural
Gas and Geopolitics: From 1970 to 2040, chapter 5, Cambridge 2006.
Von der Mehden, F and Lewis, S.W. (2006). "Liquefied natural
gas from Indonesia: The Arun project," Natural Gas and Geopolitics:
From 1970 to 2040, chapter 4, Cambridge 2006.
Yildirim, V. and Yomralioglu, T. (2007). GIS Based Pipeline Route
Selection by ArcGIS in Turkey. Twenty- seventh annual ESRI user
conference proceedings June 2007, San Diego California.
Zografos, K.G. and Androutsopoulos, K.N. (2008), "A decision
support system for integrated hazardous materials routing and emergency
response decisions", Transportation Research Part C (forthcoming).
Ghasem S. Alijani, Southern University at New Orleans
Obyung Kwun, Southern University at New Orleans
Adnan Omar, Southern University at New Orleans
Celestine Kemah, Southern University at New Orleans
Table 1: Natural gas importing and exporting countries
Exporting
Country Capacity (bcm)
Russia 237.21
Canada 107.30
Norway 86.11
Algeria 59.40
Netherlands 55.67
Turkmenistan 49.40
Qatar 39.30
Indonesia 33.13
Malaysia 31.57
Nigeria 21.21
Trinidad & Tobago 18.15
Importing
Country Capacity (bcm)
United states 130.30
Japan 95.62
Germany 88.35
Italy 73.95
France 44.56
South Korea 34.39
United Kingdom 29.19
Mexico 11.69
Turkey 35.31
Self-sufficient
Country Capacity (bcm)
Colombia 0.00
Venezuela 0.00
Azerbaijan 0.00
Iraq 0.00
Iran 0.00
Kuwait 0.00
Saudi Arabia 0.00
Bangladesh 0.00
