Opportunities for LNG Supply Infrastructure and Demand Growth in US
and International Markets
by
Richard Perry Connell
B.S. Naval Architecture and Marine Engineering
Webb Institute of Naval Architecture, 1997
SUBMITTED TO THE DEPARTMENT OF OCEAN ENGINEERING IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE IN OCEAN SYSTEMS MANAGEMENT
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
MASSACHUSETS INST IIUTE
OF TECHNOLOGY
FEBRUARY 2004
SEP 0 12005
LIBRARIES
2004 Richard Perry Connell. All rights reserved
The author hereby grants MIT permission to reproduce and to
distribute publicly paper and electronic
copies of this thesis document in whole or in part.
2
7
Signature of A uth or: ...........
.. .
.. . . . -
.................... ........
..........................
cfJepartment of Ocean Engineering
September 2, 2003
Certified by: ......................
A ccepted by : .................
..
r. H&irv S. Marcus, Professor of Marine Systems
Thesis Supervisor
..........................................................
Dr. Michael Triantafyllou, Professor of Ocean Engineering
Chairman, Department Committee on Graduate Students
...
ARCHIVES
Page Intentionally Left Blank
2
Opportunities for LNG Supply Infrastructure and Demand Growth in US
and International Markets
by
Richard Perry Connell
Submitted to the Department of Ocean Engineering in Partial Fulfillment of the
Requirements for the Degree of
Master of Science in Ocean Systems Management
ABSTRACT
Countries are looking beyond their borders for options to satiate a forecasted increase in
natural gas consumption. A strong option for importing natural gas is by way of a
liquefied natural gas (LNG) supply chain where natural gas is liquefied, transported in
special tankers, and regasified at the destination. Research was conducted to determine a
method of evaluating the feasibility of such a project.
A computer-based simulation model was created to calculate financial metrics for
potential LNG projects based on unique inputs such as annual production, distance, and
natural gas market and commodity price. Potentially feasible projects are based on the
resulting metrics as well as interpretations of risk, and a source's ability to meet a
consuming market's demand requirements. Financially, the most attractive projects were
the short haul routes to countries with high market prices. However, due to risk and
supply inadequacy, it was determined that markets with the most growth to satisfy were
best supplied by countries with the most adequate resources.
Thesis Supervisor: Dr. Henry S. Marcus
Title: Professor of Marine Systems
3
Acknowledgements
I begin by extending my sincerest thanks to Dr. Henry Marcus for his support and
guidance throughout the production of this thesis and the duration of my stay at MIT (not
to mention making this education possible). Special thanks to ConocoPhillips for
providing the means for an extraordinary research opportunity. Thanks go out to Parker
Larson for his help on this project. Thanks to Rich Gilmore of ETG for providing early
insight and informational resources. I am especially grateful to the American Bureau of
Shipping for facilitating my graduate education. Last but not least, I amorously thank
Kelly for understanding the need for this hiatus and practicing patience through it all.
4
Table of Contents
A BSTRA C T ..............................................------------------......................--
.......................................................
3
ACKNOW LEDGEM ENTS.........................................................................................................................
4
TABLE OF CONTENTS.......
5
....................--------------.------..............................................................
LIST O F FIG UR ES......................................................................................................................................7
LIST OF TABLES
............................................................
11
NOMENCLATURE AND ACRONYMS...............................................................................................
12
CHAPTER 1 - INTRODUCTION.........----.----------------...................................................................
13
CHAPTER 2 - NATURAL GAS CONSUMPTION IN THE UNITED STATES.............................
16
IN DU ST RIA L ...............................................................................................................................................
18
R ESID ENT IAI ............................................................................................................................................
21
E LECTRICITY G EN ERA TION ......................................................................................................................
23
C O M M E R C IA ...........................................................................................................................................
28
T RA N SPO RTA TION ....................................................................................................................................
28
CONSUM PTION PATTERNS & PROFILES..................................................................................................
29
FO RE C A ST S ...............................................................................................................................................
36
CHAPTER 3 - THE NATURAL GAS SUPPLY CHAIN....................................................................
38
EX PLO RA TIO N ..........................................................................................................................................
38
PRODUCTION.......................................................................................................
41
TRANSPORTATION AND D ISTRIBUTION .....................................................................................................
44
TRADITIONAL RESPONSES TO CHANGES IN CUSTOMER DEMAND..........................................................
48
CHAPTER 4 - SUPPLY AND DEMAND PORTFOLIOS OF MAJOR NATURAL GAS
CONSUM ING COUNTRIES....................................................................................................................
55
CHAPTER 5 - THE LIQUEFIED NATURAL GAS SUPPLY CHAIN.............................................71
FEEDGAS
..................................................................................
71
LIQUEFACTION PLANTS.................................................................................................................
72
LNG TANKERS........................................................................................
74
REGASIFICATION PLANTS..........................................................................
76
CHAPTER 6- THE EFFECT OF CONTRACT TERMS ON RISK AND PROJECT FEASIBILITY
PRICING STRUCTURE
........................................................................
81
8.....................................
5
TERM S OF D ELIVERY ................................................................................................................................
84
V OLUME A ciREEMENTS ............................................................................................................................
87
C HA PTER 7 - M OD EL D ESC RIPTIO N ................................................................................................ 91
M ODEL STRUCTURE .................................................................................................................................
91
LIQUEFACTI(-.)N PLANT AND TERMINAL ....................................................................................................
92
TRANSPORTATION .................................................................................................................................... 94
R EGASIFICATION PLANT ......................................................................................................................... 100
CASH FLOW A NALYSIS ........................................................................................................................... 101
D ISTANCE ................................................................................................................................................
107
C H A PTER 8 -- RESU LTS ....................................................................................................................... 109
PROJECT V IABILITY ................................................................................................................................ 110
SENSITIVITY ANALYSIS .......................................................................................................................... 116
RISK A NALYSIS ...................................................................................................................................... 122
COM MODITY PRICE O PTIM IZATION ........................................................................................................ 126
CHAPTER 9 - RECOMMENDATIONS FOR MODEL AND ANALYSIS ADVANCEMENT ...... 129
C H A PTER 10 - C O NC LU SIO N S .......................................................................................................... 133
O PTIONS FOR. THE U S ............................................................................................................................. 136
O PTIONS FOR. THE UK ............................................................................................................................. 137
O PTIONS FOR. CHINA ............................................................................................................................... 138
O PTIONS FOR M EXICO ............................................................................................................................ 138
SUMM ARY oj ' BEST O PTIONS ................................................................................................................. 139
W O R K S C ITED ....................................................................................................................................... 142
W O RK S C O N SU LTED ........................................................................................................................... 144
A PPEN D IX ................................................................................................................................................147
List of Figures
&
Figure 1 - US Natural Gas Consumption, 1949 - 2001 [EIA (3), 2002]...................... 16
Figure 2 - US Natural Gas Consumption by Sector, 1990 - 2001[EIA (3), 2002]........ 17
Figure 3 - US Natural Gas Consumption by Sector, 1990 [EIA (3), 2002]................... 17
Figure 4 - US Natural Gas Consumption by Sector, 2001 [EIA (3), 2002]................... 18
Figure 5 - Percent of Total Natural Gas Consumption for Selected Industries, 1991
1994 [EIA (9), 1994].......................................................................................
.. 19
Figure 6 - Industrial Natural Gas Consumption by End Use [EIA (9), 1994].............. 19
Figure 7 - Net Electricity Generation, 1949 - 2001. [EIA (10), 2002]........................ 24
Figure 8 - Breakdown of Net Electricity Generation Growth by Power Source, 1990-2001
[E IA (10), 2002]....................................................................................................
24
Figure 9 - Existing Net Electricity Generation Breakdown by Power Source, 2001.
Electricity generation sector only [EIA (10), 2002] .............................................
25
Figure 10 - Breakdown of Changes in Installed Generating Capacity by Power Source As
a Percent of Total Generation Capacity Growth, 1990-2001. Electricity generation
sector only. [EIA (10), 2002]...............................................................................
26
Figure 11 - Monthly Natural Gas Consumption by Sector [EIA (11), 2002]............... 29
Figure 12 - Monthly Industrial Gas Consumption in Selected States, January 2000 to
O ctober 2002 [E IA] .............................................................................................
31
Figure 13 - Monthly Industrial Gas Consumption in Selected States (not including CA
and TX), January 2000 to October 2002 [EIA] ....................................................
31
Figure 14 - Monthly Residential Gas Consumption in Selected States, January 2000 to
October 2002 [E IA ] ...............................................................................................
33
Figure 15 - Monthly Electric Utility Gas Consumption in Selected States, January 2000 to
October 2002 [EIA ] ...............................................................................................
35
Figure 16 - Monthly Commercial Gas Consumption in Selected States, January 2000 to
O ctober 2002 [EIA ] ...............................................................................................
36
Figure 17 - Forecasted Natural Gas Consumption by Sector to 2025 in Trillion Cubic Feet
[EIA (1),2003]....................................................................................................
. . 37
Figure 18 - The Natural Gas Supply Chain in General Terms.................................... 38
Figure 19 - Classification Breakdown of Oil and Natural Gas Resources [EIA (5),2001]
.........................................................................................................
40
Figure 20 -Simplified Stock and Flow Diagram of the US Domestic Natural Gas Supply
Chain ............................................................................................
.
........ 42
Figure 21 - New Developmental Wells Drilled per Year per Commodity [EIA]......... 43
Figure 22 - Graph of Domestic Natural Gas Production and Productive Capacity (BCF
per day), 1995-2002 [FERC (1), 2003], [AGA] ....................................................
44
Figure 23 - Types of Underground Storage Facilities [EIA (4), 2002] ........................ 47
Figure 24 - Stock and Flow Diagram of the Natural Gas Supply Chain's Response to
Immediate Changes in Demand...........................................................................
49
Figure 25 - Stock and Flow Diagram of the Natural Gas Supply Chain's Response to
Short-term Forecasted Changes in Demand ........................................................
51
Figure 26 - Stock and Flow Diagram of the Natural Gas Supply Chain's Response to Midterm Forecasted Changes in Demand ....................................................................
52
Figure 27 - Stock and Flow Diagram of the Natural Gas Supply Chain's Response to
Long-term Forecasted Changes in Demand.........................................................
54
7
Figure 28 - Breakdown of Natural Gas Consumption in Belgium by Sector, 2000 [IEA
(1), 2 0 0 2]...................................................................................................................
55
Figure 29 - Trends in Monthly Natural Gas Consumption, Imports, and Storage Changes
in Belgium , 1998-2000 [IEA (1), 2002] ...............................................................
56
Figure 30 - Breakdown of Natural Gas Consumption in France by Sector, 2000 [IEA (1),
2 0 02 ] .........................................................................................................................
57
Figure 31 - Monthly Consumption Patterns of France's Major Natural Gas Consumers,
1998-2000 [IEA (1), 2002]....................................................................................
57
Figure 32 - Trends in Monthly Natural Gas Consumption, Production, Imports, and
Storage Changes in France, 1998-2000 [IEA (1), 2002] ......................................
58
Figure 33 - Breakdown of Natural Gas Consumption in Greece by Sector, 2000 [IEA (1),
2 0 0 2 ] .........................................................................................................................
59
Figure 34 - Trends in Monthly Natural Gas Consumption, Production, and Storage
Changes in Greece, 1998-2000 [IEA (1), 2002]....................................................
59
Figure 35 - Breakdown of Natural Gas Consumption in Italy by Sector, 2000 [IEA (1),
2 0 02 ] .. .......................................................................................................................
60
Figure 36 - Monthly Consumption Patterns of Italy's Major Natural Gas Consumers,
1998-2000 [IEA (1), 2002]....................................................................................
60
Figure 37 - Trends in Monthly Natural Gas Consumption, Production, Imports, and
Storage Changes in Italy, 1998-2000 [IEA (1), 2002]...........................................
61
Figure 38 - Breakdown of Natural Gas Consumption in Japan by Sector, 2000 [IEA (1),
2 0 0 2 ] .........................................................................................................................
62
Figure 39 - Monthly Consumption Patterns of Japan's Major Natural Gas Consumers,
1998-2000 [IEA (1), 2002]....................................................................................
62
Figure 40 - Trends in Monthly Natural Gas Consumption, Production, Imports, and
Storage Changes in Japan, 1998-2000 [IEA (1), 2002].........................................
62
Figure 41 - Breakdown of Natural Gas Consumption in Korea by Sector, 2000 [IEA (1),
2 0 0 2 ] .. .......................................................................................................................
63
Figure 42 - Monthly Consumption Patterns of Korea's Major Natural Gas Consumers,
1998-2000 [IEA (1), 2002]....................................................................................
64
Figure 43 - Trends in Monthly Natural Gas Consumption, Imports, and Storage Changes
in K orea, 1998-2000 [IEA (1), 2002] ....................................................................
64
Figure 44 - Breakdown of Natural Gas Consumption in Portugal by Sector, 2000 [IEA
(1), 2 0 0 2 ]...................................................................................................................
65
Figure 45 - Trends in Monthly Natural Gas Consumption, Imports, and Storage Changes
in Portugal, 1998-2000 [IEA (1), 2002]................................................................
66
Figure 46 - Breakdown of Natural Gas Consumption in Spain by Sector, 2000 [IEA (1),
2 0 0 2 ] .........................................................................................................................
66
Figure 47 - Trends in Monthly Natural Gas Consumption, Production, Imports, and
Storage Changes in Spain, 1998-2000 [IEA (1), 2002].........................................
67
Figure 48 - Breakdown of Natural Gas Consumption in the United Kingdom by Sector,
2000 [IE A (1), 2002]..............................................................................................
68
Figure 49 - Quarterly Consumption Patterns of the United Kingdom's Major Natural Gas
Consumers, 1998-2000 [IEA (1), 2002]...............................................................
69
Figure 50 - Trends in Monthly Natural Gas Consumption, Production, Imports, and
Storage Changes in the United Kingdom, 1998-2000 [IEA (1), 2002]................ 69
8
Figure 51 - S-curve Created From Instituting a Price Ceiling and Price Floor ............. 83
Figure 52 - Monthly Natural Gas Consumption By Sector [EIA (11), 2002] .............. 88
Figure 53 - Average Monthly Natural Gas Prices per Consumer Sector, 2001-2003 [EIA
(1 1),2 0 0 2 ].................................................................................................................89
Figure 54 - Average Monthly Wellhead and City Gate Natural Gas Prices, 2000-2003
[EIA (I1 ), 2002]..............-- - - -. --..... ................ ..............................................
89
Figure 55 - Liquefaction facility capital costs normalized per ton of annual output........ 93
Figure 56 - Liquefaction facility annual operating costs normalized per ton of annual
ou tput ......................................................................................................
.... 94
Figure 57 - Trends in newbuilding prices normalized for capacity (left axis) and
calculated for a 137,500 cubic meter tanker [MBS].............................................
96
Figure 58 - Trend in normalized capital costs for regasification facilities ..................... 101
Figure 59 - LNG supply chains with the most favorable NPV...................
111
Figure 60 - NPV of import options to Baltimore, MD under base case conditions........ 112
Figure 61 - LNG supply routes with the highest internal rate of return under base case
con d ition s ................................................................................................................
1 13
Figure 62 - Internal rate of return for export locations to import into Baltimore under base
case condition s ........................................................................................................
1 14
Figure 63 - Capital expense required for trade routes importing to Baltimore under base
case condition s ........................................................................................................
115
Figure 64 - NPV sensitivity of increasing LNG imports into Baltimore by 1 mmtpa under
base case conditions................................................................................................
1 17
Figure 65 - IRR sensitivity of increasing LNG imports into Baltimore by lmmtpa under
base case conditions................................................................................................
118
Figure 66 - IRR sensitivity to a 100% increase in imported LNG into Baltimore under
base case conditions................................................................................................
118
Figure 67 - Minimum and maximum NPV's a range of discount rates for import routes
into the UK under base case conditions..................................................................
119
Figure 68 - NPV sensitivity to a 1 point discount rate reduction on UK import routes
under base case conditions......................................................................................
120
Figure 69 - Average net income sensitivity to a 10% increase in contract price to all
import m arkets under base case conditions ............................................................
121
Figure 70 - Average IRR sensitivity to a 10% increase in contract price to all import
m arkets under base case conditions ........................................................................
122
Figure 71 - Net income sensitivity to a 10% increase in the tax rate for LNG imports into
Baltim ore under base case conditions.....................................................................
123
Figure 72 - IRR sensitivity to a 10% increase in the tax rate for LNG imports into
Baltim ore under base case conditions.....................................................................
124
Figure 73 - Average net income sensitivity to a 10% increase in commodity price on
imports to all markets under base case conditions..............................................
125
Figure 74 - Average IRR sensitivity to a 10% increase in commodity price on imports to
all markets under base case conditions ...................................................................
125
Figure 75 - commodity price reduction required by export nations to compete with the
best NPV exporters for import into Baltimore........................................................
127
Figure 76 - Commodity price required by Trinidad to match each import market's best
N PV source option .............................................................................................
128
9
Figure 77 - Commodity price required in Dampier, Australia to match all import markets'
best NP V source......................................................................................................
128
Figure 78 - The 30 Best Supply Routes in Terms of IRR Under Base Case Conditions 133
10
List of Tables
Table 1 - Standard Deviation as a Measure of Volatility of Monthly Industrial Gas
Consum ption. Reference Figure 13 .........................................................................
32
Table 2 - Correlations of State Monthly Industrial Gas Consumption. Reference Figure
13 ......................................- -- -- -.......--- --- --- ..............................................................
32
Table 3 - Standard Deviation As a Measure of Volatility of Monthly Residential Gas
Consumption. Reference Figure 14 ......................................................................
34
Table 4 - Standard Deviation As a Measure of Volatility of Monthly Residential Gas
Consumption. Reference Figure 15 ......................................................................
35
Table 5 - Standard Deviation As a Measure of Volatility of Monthly Commercial Gas
Consumption. Reference Figure 16 ......................................................................
36
Table 6- Interstate Pipeline Utilization per State During Their Peak Demand Month of
2000. All measurements in MMCF/day [Tobin, 2001] ........................................
48
Table 7 - LNG Tanker Cargo Containment Systems [Greenwald, 1998]..................... 75
Table 8 - LN G Term s of Delivery .................................................................................
84
Table 9- Candidate tanker capacities and cruising speeds...........................................
98
Table 10 - LNG tanker model payment schedule ...........................................................
102
Table 11 - Contract price selections for the model's base case. All prices in $/mmBtu
[IE A (2), 2002], [E IA ] ............................................................................................
106
Table 12 - Import and export locations selected for inclusion in the model................... 108
Table 13 - User Specific Purposes for Using a Feasibility Model................................. 109
Table 14 - Natural Gas Consumption Estimates of Selected Countries. Units: trillion
cubic feet per year...................................................................................................
134
Table 15 - Estimate of Natural Gas Reserves in 2030 without Existence of Additional
LN G Exports [IEA (2), 2002].................................................................................
136
Table 16 - Import Locations Matched with Optimal Supply Locations by Considering
Financial Metrics, Demand Forecasts, Supply Estimates, and Risks Associated with
Security of Supply...................................................................................................
140
Table 17 - Model Results for Five Best Trade Routes. Based on IRR Under Base Case
C on d ition s ...............................................................................................................
14 8
Table 18 - Model Results for Five Best Trade Routes. Based on Highest NPV Under Base
C ase C onditions ......................................................................................................
149
Table 19 - Model Results for Five Best Trade Routes. Based on Lowest CapEx Under
B ase C ase C onditions .............................................................................................
150
Table 20- Model Results for Five Best Trade Routes. Based on Highest Net Income
Under B ase C ase C onditions ..................................................................................
151
Table 21 - Distance Between Ports in Nautical Miles (Import Along the Left & Export
A long the R ight) ...................................................................................................
152
11
Nomenclature and Acronyms
AGA - American Gas Association
BANANA - build absolutely nothing anywhere near anything
bcf - billion cubic feet of natural gas
bcm - billion cubic meters of natural gas
CapEx - capital expense
CHP - combined heat and power plant
CIF - Cost, Insurance, and Freight
cm - cubic meters of LNG
DEQ - Delivered Ex-Quay or "tailgate"
DOE - Department of Energy
DOT - Department of Transportation
EIA - Energy Information Agency
EPC - Engineering, Procurement, and Construction
FEED - Front End Engineering and Design
FERC - Federal Energy Regulatory Commission
FOB - Free-On-Board
IEA - International Energy Agency
IEEJ - Institute of Energy Economics, Japan
IPP - independent power producer
IRR - internal rate of return
LNG - liquefied natural gas
mcf - thousand cubic feet of gas
mcm - thousand cubic meters of gas
mmbtu - million British thermal units
mmcf - million cubic feet of gas
mmtpa - million tons of LNG per year
NIMBY - not in my back yard
NPV - net present value
OPS - Office of Pipeline Safety
SPA - Sale and Purchase Agreement
TCF - trillion cubic feet of natural gas
12
Chapter 1 - Introduction
In recent years energy shortages and surging gas prices have brought public
attention to the fragility of the natural gas market in the United States. Gas consumption
in the US has been steadily rising, and the infrastructure required to handle the gas supply
has seen increased utilization. Unfortunately, infrastructure expansion and improvement
have not kept pace with consumption growth. Today, stake holders in the US natural gas
market are analyzing their options for supply and infrastructure enhancements that will
support consumption that is expected to grow almost 50% by year 2020.
Internationally, countries are restructuring their energy portfolios to reap the
benefits of natural gas. In nearly all well developed countries, pollution restrictions are
encouraging the shift to clean burning natural gas. In most of these countries, this
requires development of new infrastructure to handle the commodity. While other
countries require import programs to extend the life of indigenous supplies while
complementing existing gas infrastructure. Developing nations are interested in using
natural gas for power to spur economic and residential development. In most cases,
decision makers are faced with the option to develop indigenous reserves in primitive
regions or accept delivery of imported natural gas.
These issues facing international and domestic markets are forcing decision
makers to accept the need for imported natural gas. Importing the gas can occur by way
of two proven means: pipelines and tankers. Both means require a sophisticated supply
chain to link suppliers and customers; however, transporting natural gas on a tanker
becomes the cost-effective solution when long distances, oceans, or rugged terrain
separate supply and demand.
13
The purpose of this research is to develop a means for evaluating the feasibility of
developing liquefied natural gas (LNG) supply chains. The developed method consists of
a computer-based simulation model capable of calculating financial metrics for a variety
of user inputs. The resulting metrics are combined with information regarding available
supply, consumer demand, and potential risk to determine supply sources that would be
most attractive for an LNG supply project.
The discussion of the research begins with a thorough analysis of natural gas
consumption in the United States as described in Chapter 2. This includes examination
of the factors affecting consumption within the many consuming sectors. Chapter 3
consists of a general explanation of the natural gas supply chain in an effort to capture the
logic behind introducing LNG. This discussion is followed by Chapter 4 which presents
consumption and supply information for selected countries. Although it is essentially a
country by country summary, this discussion is similar in nature to the supply and
demand issues addressed in the previous chapters. The LNG supply chain is described in
Chapter 5 with focus given to details having the greatest effect on cash flows, risk, and
the overall viability of the project. Chapter 6 presents the major risks inherent in LNG
projects and how the Sale and Purchase Agreement (SPA) addresses these risks. Chapter
7 is a thorough description of the model used for financial evaluation including the model
structures, optimizations, and inputs. Chapter 8 interprets results generated by the model.
The relevance of these financial metrics is explained with regards to various user groups.
Recognizing that the model is far from perfect, Chapter 9 presents opportunities for
improvement within the model and the analysis of LNG projects in general. Conclusions
in Chapter 10 focus on determining the most feasible match of import market with gas
14
supply source based on a combination of model results, risk analysis, and the sources'
ability to meet the customers' demands.
15
Chapter 2 - Natural Gas Consumption in the United States
The US Department of Energy's (DOE) Energy Information Agency (EIA)
estimates 22.6 trillion cubic feet (TCF) of natural gas were consumed in 2001. Based on
common intuition of US energy dependence, one would think that this is the highest level
of natural gas consumption ever. Furthermore, one would think that it dwarfs
consumption from a decade earlier let alone a quarter century ago. However, this
intuition is somewhat incorrect. As shown in Figure 1, the US has only been consuming
natural gas at record rates since 1995. Consumption peaked in 1973 during the Arab Oil
Embargo; however it took over 20 years to return to that level. Furthermore, the average
rate of increase of consumption since 1986 is only half of the 0.75 trillion cubic feet/year
average increase experienced from 1949 to 1973.
24
1973 - Oil Embargo
N
4A
21
V
18
=12
:E
9
6
1949
1953
1957
1961
1965
1969
1973
1977
1981
1985
1989
1993
1997
2001
Figure 1 - US Natural Gas Consumption, 1949 - 2001 [EIA (3), 2002]
Focusing on recent history, consumption has grown a steady 1.6% per year from
19.2 trillion cubic feet of natural gas consumed in 1990. Natural gas consumption (i.e.
16
demand) is segregated into five sectors of consumers. The sectors are as follows:
Industrial, Residential, Electricity Generation, Commercial, and Transportation. Figure 2
shows the breakdown of consumption by sector since 1990. Figure 3 and Figure 4 show
the percent breakdown of sectors in 1990 and 2001.
25
20
15
*Transportation
OElectric Power
Olndustrial
MCommercial
EResidential
10
0M
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Year
Figure 2 - US Natural Gas Consumption by Sector, 1990 - 2001 [EIA (3), 2002]
Electric Power
17%
Residential
23%
Transportation
3%
Commercial
14%
Industrial
43%
Figure 3 - US Natural Gas Consumption by Sector, 1990 [EIA (3), 20021
17
Electric Power
23%
Transportation
3%
Commercial
14%
Industrial
39%
Figure 4 - US Natural Gas Consumption by Sector, 2001 [EIA (3), 2002]
Industrial
Industrial consumers are traditionally the largest sector consuming 8.69 trillion
cubic feet of gas in 2001. [EIA (3), 2002] This group, as seen in Figure 5, consists
mainly of mills, refineries, and manufacturing facilities. There is also a small amount of
industrial consumption from combined-heat-and-power (CHP) facilities and independent
power producers (IPP's). CHP's and IPP's who focus on only industrial customers are
included in this sector. Figure 6 shows the breakdown of natural gas consumption by end
use. Although still considered "industrial," CHP's and IPP's consumed 35% of the gas
burned in boilers in 1991 and 1994. CHP/IPP gas consumption grew 27% between 1991
and 2001.
18
37
38
Chemical
Petroleum
Q1214
12
12
Primary Metal
D 1991
Food
m 1994
Paper
8
Stone, Clay, and Glass
6
Fabricated Metal
O
1'0
2'0
h'
Percent
40
Figure 5 - Percent of Total Natural Gas Consumption for Selected Industries, 1991 & 1994 tEIA (9),
19941
i42
P rocess
42
34
Bailer Fuel
El 1991
M 1994
112
N onprocess
11
Feedstock
10
0
10
20
30
40
50
Percent
Figure 6 - Industrial Natural Gas Consumption by End Use [EIA (9), 19941
An industrial consumer's energy requirements are affected by the following
factors:
0
Changes to industrial output
" Improvements in technology
" Changes in oil/gas prices
Changes to industrial output
As production increases, energy requirements should increase. This is especially
true as facilities produce beyond optimal levels and lose efficiency. Larger increases in
19
production require more expansion of facilities. Building new production lines and
factories increases the base load of gas and energy required.
Improvements in technology
Through most of the twentieth century, improvements and developments in
technology resulted in machines replacing labor. With so many more machines to
operate, power requirements increased. However, today there are fewer opportunities to
replace existing manual labor with machines. In fact, computerized machines that
produce more efficiently and require less energy input are replacing older, inefficient
machines. Although this power savings is most obviously true for electrically powered
equipment, sophisticated computerized controllers improve the efficiency of gas process
equipment.
Large industrial parks are becoming more efficient power producers. As
mentioned earlier, CHP's and IPP's are increasingly used for the power and heating
needs of industrial facilities. They produce more efficiently and at a larger scale than
individual industrial facilities. To protect against large price swings in fuel costs, many
industrial consumers are installing steam/power generating and process equipment that
permits easy switching from oil to gas. This allows the plant to substitute and bum the
cost efficient fuel. In addition, industrial parks do not suffer the line losses inherent in a
large distribution grid.
Fluctuations in oil and gas prices
Although industrial demand contributes to the pricing of fossil fuels, other factors,
alone or in tandem, affecting aspects of supply and demand can have a large immediate
effect on prices. Short-term response to changes in prices may be changes in production
20
levels, switching fuels in a dual fuel plant, or hedging against further changes with
financial derivatives. Medium-term response may include small investment in facility
efficiency improvements. Long-term responses include significant changes in the means
of production, installation of dual fuel capability, or new equipments for complete
substitution of fuel used. Since energy prices can have a large influence on a
manufacturer's profitability, there is incentive in investment that reduces the total cost of
gas and/or power. Therefore industrial gas consumers will typically respond to changes
in gas prices. This was evidenced in the winter of 2000/2001 as ballooning gas prices
caused industrial consumers to re-examine their energy needs. Extreme cases saw many
industrial gas consumers with fixed priced contracts finding it more profitable to reduce
or halt production in order to sell their lower priced gas on the open market. [EIA (8),
2001]
Residential
Residential consumers use natural gas predominantly for home heating purposes (i.e.
air, water, and cooking). Their consumption of natural gas is affected by:
" Weather
" Home growth
" Public perception
" Gas prices
21
Weather
Since heating is the main use of natural gas, cold weather is the major influence
on consumption. Residential consumers expect to use increased amounts of gas during
winter months.
Home Growth
Increased levels of home ownership and larger homes in general increase the need
for heating fuels. More homes are built in order to shelter a growing population and to
accommodate the population's increasing desire to move from apartments to houses.
Since homes are larger, residents have a need to heat more space. This need is enhanced
as the average size of new homes increases. From 1996-1999, the average new home
was built with 22% more square footage. [EIA (8), 2001]
Public Perception
Natural gas competes with electricity and oil in the residential market. While it
may be cost effective for apartment complex builders and owners to use electricity,
homeowners typically prefer fossil fuels for heating. Because it is more environmentally
friendly and a less obvious fuel (i.e. no soot, oil tanks, or delivery issues), natural gas has
enjoyed preference over oil. In fact, from 1986 to 1999, the percentage of new homes
constructed with natural gas capability increased from 46% to 77%. [EIA (8), 2001]
Gas prices
Since people need to eat and stay warm, residential consumers have limited
ability to respond to changes in natural gas prices. Short-term responses to higher prices
include dressing warmer, repairing windows, and using less heat. Medium to long-term
22
responses may include replacing furnaces and thermostat systems with more efficient
units. Because these responses have a limited effect, residential consumers are
considered "captive." As a result, residential consumers' potentially most effective tool
against high gas bills is through political channels. Although the federal government has
deregulated most of the gas and electricity markets, the government through legislation or
subsidies is committed to keeping prices relatively stable over the long term.
Electricity Generation
The electricity generation sector is the fastest growing consumer sector having
grown 63% from 3.23 TCF in 1990 to 5.26 TCF in 2001. This is impressive growth
considering net electricity generation only increased 23% over the same time period
(reference Figure 7). Figure 8 shows the breakdown of power sources comprising this
growth. Factors encouraging added electricity generation include population growth and
inflation adjusted GDP growth, which grew 13% and 32% respectively over the same
period. Although electricity generation and gas consumption has shown significant
growth, natural gas is used to produce only 15% of the nation's electricity as shown in
Figure 9.
23
4-
Total
(All Sectors)
30
Electricity-Only
Plants
Z:
0
Combined-Heat-and-Power r
Plants
0
-1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Figure 7 - Net Electricity Generation, 1949 - 2001. [EIA (10), 20021
-20%
-10%
0%
20%
10%
30%
40%
50%
% of Growth
Figure 8 - Breakdown of Net Electricity Generation Growth by Power Source, 1990-2001 [EIA (10),
20021
24
6%
1%
353
Figure 9 - Existing Net Electricity Generation Breakdown by Power Source, 2001. Electricity
generation sector only [EIA (10), 20021
The following factors are making natural gas a more attractive option:
"
Pollution restrictions
"
Failure of other energy sources
*
Sophisticated power requirements
Pollution Restrictions
Pollution legislation enacted in the 1990's has increased restrictions on a power
plant's ability to pollute. Although coal is an inexpensive and abundant domestic
resource, the cost of cleaning exhaust and/or purchasing higher pollution quotas is
making coal an expensive option outside of baseload generating. Natural gas has become
the clean burning fossil fuel alternative to coal.
25
Failure of other Energy Sources
Although nuclear power contributes significantly to power generation in the US
and capacity utilization is high, firm plans for new capacity are nonexistent due to a lack
of public support and prohibitive construction and operating costs. The strong gain in
nuclear electricity generation shown in Figure 8 is 100% attributable to increased
utilization of existing facilities. Hydroelectric power has long been considered an
effective alternative to fossil fuels and has become a major source of power for
consumers in the Pacific Northwest. However, since 1996 the reliability of hydroelectric
power has been tested by low water levels in source rivers. This phenomenon has put a
large strain on the available supply of power on the West Coast and contributed largely to
the energy shortages of 2000 and 2001. Again referring to Figure 8, hydroelectric
generation decreased from 1990 to 2001. Referencing Figure 10, the long-term response
to high generating costs has been the construction of natural gas fired power plants.
-20%
-10%
0%
10%
20%
30%
40%
W0%
60%
70%
% of Gr owt h
Figure 10 - Breakdown of Changes in Installed Generating Capacity by Power Source As a Percent
of Total Generation Capacity Growth, 1990-2001. Electricity generation sector only. [EIA (10),
20021
26
Sophisticated Power Requirements
When analyzing the electricity requirements for a region over a decade,
consumption is relatively flat with perhaps signs of steady growth or a divot during an
energy crisis. When analyzed over a few years, the seasonal peaks and valleys appear.
However, when analyzed over the course of a day or two, there is the potential for great
volatility. There are numerous reasons for this volatility including use of lights at night
and operating the air conditioner at home overnight. Whatever the requirement, electric
power providers are obliged to ensure that the electricity is available. This creates the
separation of baseload and peak operating conditions. Baseload is determined through
standard minimum conditions. Regardless of the time of day, there is a minimum
predictable power requirement for all consumer groups in a given area and season.
Electricity generators typically meet this requirement with steam or hydroelectric
generating capacity. Steam plants utilize coal, nuclear, and sometimes natural gas to
generate power. Due to the nature of steam plant technology, it is only efficient when
generating a steady output. Similarly, hydroelectric power utilizes a steady source of
water that is almost always flowing.
Peak conditions are met by generating equipment capable of flexible operations.
As opposed to steam plants, natural gas powered turbines can be brought on and offline
without sacrificing a great deal of efficiency. Although nuclear and coal-fired steam
plants are typically a more cost effective baseload solution, gas turbines are replacing
steam plants and diesel engines to satisfy peak requirements.
27
Commercial
Commercial consumers include firms involved in business but not directly
involved in a factory environment. Typically, commercial consumers reside in office
buildings, malls, or even homes. Similar to residential, commercial consumers require
gas largely for heat and less for business services. For these reasons, commercial gas use
is affected largely by weather. As firms grow, they require more gas; however, net
growth for commercial consumers should correlate well with growth of employees in the
active workforce. Over the period 1990 to 2001, active employment rose 15% while
commercial sector natural gas consumption rose 24%.[BLS] The difference in growth
can be attributed to the economic benefits of natural gas that make it the preferred energy
source for new installations. Since costs related to gas are relatively low compared to
other business costs and substitution is not a ready option, these consumers are "captive"
to gas prices. Another factor, although difficult to gauge, is the transformation of US
industry from one oriented to physical "production" to one moving towards "service."
Therefore, it is likely that jobs will move from the industrial to the commercial sector.
Gas consumption should follow accordingly.
Transportation
The transportation sector has two main uses for natural gas. Natural gas
consumed to power pumps and compressors for the transportation of pipeline fuels (i.e.
predominantly gas but also oil) comprises 98% of the transportation sector's
consumption. The remaining gas is consumed in vehicle engines. Although vehicle
transportation natural gas consumption is relatively insignificant, it is a new, fast growing
28
concept. Consumption grew 525% from 1992 to 2001. Considering the rapid growth, it
is likely that the EIA's reporting system is somewhat inaccurate and still catching up with
new end-users. Overall, transportation sector consumption has been steady since the
1970's at 3% of total gas consumption.
Consumption Patterns & Profiles
The factors discussed above heavily influence consumer gas consumption;
however, the nature of these factors, whether cyclic, random, or steady, contributes to a
more accurate assessment of a consumer sector's consumption profile. Consumption
profile information is critical to the design of a gas distribution company's supply chain.
The following subsections discuss the trends in sector consumption as evidenced by
Figure 11.
1.2Residental
0.9-
Induattial
0.6-
0.3-
-
0Commtrcal
002002
Figure 11 - Monthly Natural Gas Consumption by Sector [EIA (11), 2002]
Industrial
The major factors that promote change in overall industrial consumption are
largely predictable and stable. This makes industrial consumption a cornerstone of
baseload gas consumption. Since gas companies are interested in a large base level of
29
production, gas companies attract industrial customers with long-term contracts
consisting of favorable discounted gas prices and service flexibility. Although national
industrial consumption as shown in Figure 11 appears relatively steady, regional
consumption profiles do vary as evidenced by state data in Figure 12 and Figure 13.
Both figures clearly demonstrate that there is a significant deal of volatility ranging from
standard deviations of growth of 8% to 26% (see Table 1). However, as referenced in
Table 2, the individual profiles show poor correlation with each other. In fact there is not
a single correlation above 0.5 with 1 being perfect correlation. Although these figures are
based on less than 2 years of monthly data, the figures reinforce the concept of stable
industrial gas consumption for the following reasons:
*
Variation in national economy: early 2000 was strong but late 2001/2002 was
poor
*
Due to poor correlation and relatively low volatility, industrial gas consumption
as measured nationally is "diversified." As per Table 1, the national standard
deviation is 5%.
There are two major points to take out of the behavior behind industrial gas consumption:
1. Gas companies with customer interests located nationally, as opposed to
just regionally, can expect steady consumption from industrial consumers
regardless of the state of the economy.
2. Gas companies with industrial customers located regionally or locally are
subject to greater volatility of industrial consumer demand.
Both of these points influence the decisions behind the composition of natural gas supply
chain infrastructure.
30
0.25
-*-CA
0.15
-U-FL
MA
-X-NJ
---
NY
-TX
---MI
S0.05
00
Figure 12 - Monthly Industrial Gas Consumption in Selected States, January 2000 to October 2002
[EIA]
0.04
0.035
&0
--LL
FL
0035
0.02
MA
-4-NJ
-W-NY
-
M'
0.005
Figure 13 - Monthly Industrial Gas Consumption in Selected States (not including CA and TX),
January 2000 to October 2002 [EIA]
31
Standard Deviation
CA
FL
MA
NJ
NY
TX
M1
13%
8%
26%
11%
12%
8%
10%
USA
5%
Table 1 - Standard Deviation as a Measure of Volatility of Monthly Industrial Gas Consumption.
Reference Figure 13
CA
FL
MA
NJ
NY
FL
MA
NJ
NY
TX
M1
-0.213
0.152
-0.331
0.315
0.429
-0.511
0.104
0.456
0.492
0.065
0.157
0.341
0.427
0.276
0.447
0.252
-0.351
0.360
0.095
-0.065
TX
0.060
Table 2 - Correlations of State Monthly Industrial Gas Consumption. Reference Figure 13
Residential
The major factor affecting residential consumption, seasonal weather, is
somewhat predictable; however, the level of consumption over daily and weekly periods
during the winter can be unpredictable. Extreme cold snaps create spikes in the
residential power profile and often come with only a few days warning.
By comparing national residential consumption as shown in Figure 11 and state
consumption as shown in Figure 14, the correlation of winter peaks and summer lows is
obvious. Geographic differences contribute to the level and duration of peak usage. In
Figure 14, California has the highest monthly consumption. Since the weather in most of
California is considered to be stable year round, the peaks are influenced by the shear size
of the state's population. Comparing the profiles in Figure 14 by calculating standard
deviations of monthly growth is proven to be a better means of judging state consumption
32
volatility. Referencing Table 3, California and Florida enjoy the lowest volatility while
the northern states are predictably more volatile. Texas has the highest volatility at 49%
that at first seems awkward. However, it has been the author's experience that although
Texas has the reputation for temperate weather, North and West Texas typically
experience freezing winter weather. This coupled with typically warm fall and spring
seasons that require little gas heat contribute to a high standard deviation.
Lastly, the correlation of consumption amongst states is clearly high. This
explains the high volatility of national residential consumption (see Table 3) that does not
benefit from a diversification of different consumers. Therefore, gas companies serving
local or national customers are faced with comparable residential customer volatility but
varying, yet predictable, levels of peak consumption. Regardless of the customer base,
gas companies must have infrastructure capable of efficiently handling these peaks.
0.09
0.08
0.07---
0.06
Q
0.04
CA
o~oe
FL
F
-m-
MA
-
NY
0.03
S0.02
0.01
Figure 14 - Monthly Residential Gas Consumption in Selected States, January 2000 to October 2002
[EIAI
33
Standard Deviation
CA
FL
MA
NJ
NY
TX
MI
USA
26%
34%
42%
46%
39%
49%
52%
45%
Table 3 - Standard Deviation As a Measure of Volatility of Monthly Residential Gas Consumption.
Reference Figure 14
Generation
As discussed earlier, non-baseload electric power demand drives consumption of
natural gas for electric power producers. This demand typically occurs in the summer
and correlates oppositely to residential and commercial gas consumption (see Figure 11).
Similar to the residential situation, consumption amongst states is well correlated. For
this reason, gas companies find it difficult to minimize volatility through diversification.
Although consumption across states is well correlated, the volatility as seen in Table 4 is
not as severe as in the residential sector. This can be attributed to the greater importance
of heating to cooling. In almost all states, homes are built with a means of space heating.
This is definitely not true for air conditioning equipment. However, as the cost of air
conditioning equipment declines, it is likely to see more new homes built with a/c
capacity. In conclusion, similar to winter needs, gas companies must ensure
infrastructure exists to supply power plants during the hot summer months.
34
0.18
0.16
0.14
*
o
0.12
0.1
0.08
-.-
u
CA
FL
FL
__---NY
TX
0.06--M
0.04
0.02
0
Figure 15 - Monthly Electric Utility Gas Consumption in Selected States, January 2000 to October
2002 [EIA]
State
Standard Deviation
CA
FL
NY
TX
MI
USA
28%
18%
26%
24%
54%
20%
Table 4 - Standard Deviation As a Measure of Volatility of Monthly Residential Gas Consumption.
Reference Figure 15
Commercial
Similar to residential consumers, commercial consumers are affected mostly by
weather. Commercial demand is not as high as residential demand for two main reasons:
1. more of the entire population contributes to residential demand than commercial
demand. 2. commercial properties are typically more efficient and have less square
footage/person to heat. Figure 16 illustrates regional profiles of commercial
consumption. Similar to the residential consumer profile, there is not much of an
opportunity to diversify commercial demand because state demand is well correlated.
The standard deviations given in Table 5 are less than those for residential consumption;
35
however, the volatility of US commercial consumption is still closer to a national average
because there are no benefits of diversification. In order to satisfy commercial customers
during the winter months, gas companies must size their supply chain infrastructure to
handle the added demand.
0.06
0.05
-
I.0.04
-m- MA
0.03
0
CA
-i-MI
NJ
-
0.02
NY
0.01
0
Figure 16 - Monthly Commercial Gas Consumption in Selected States, January 2000 to October 2002
[EIA]
State
CA
FL
MA
MI
NJ
NY
TX
USA
Standard Deviation
14%
9%
30%
36%
35%
16%
25%
26%
Table 5 - Standard Deviation As a Measure of Volatility of Monthly Commercial Gas Consumption.
Reference Figure 16
Forecasts
As indicated in Figure 17, the EIA predicts that most consumer sectors will
steadily consume more natural gas. The exception is the electricity generation sector
which is expected to enjoy strong growth. The impetus behind this growth is the
assumption that lower pollution limits and gas power plant efficiency improvements will
36
encourage power producers to drop coal in favor of natural gas for baseload generation.
In 2001, 22.6 trillion cubic feet of natural gas was consumed in the United States. The
EIA predicts that 27.1 trillion cubic feet will be consumed in 2010 and 32.1 in 2020.
This is equivalent to 2% annual growth until 2010 and 1.7% annual growth from 2010 to
2020.
1-
Hj.,an-,
I A
.1 t
4: .pi
Mt
Figure 17 - Forecasted Natural Gas Consumption by Sector to 2025 in Trillion Cubic Feet [EIA
(1),20031
37
Chapter 3 - The Natural Gas Supply Chain
To justify an LNG import project, it must be proven that the LNG supply chain is
cost competitive with other sources of natural gas. Sources are located onshore or
offshore within domestic or foreign jurisdiction. Regardless of their location or means of
extraction, all sources share a common general supply chain. Presented very generally in
Figure 18, the supply chain is segmented into four principal groups with Consumption
having been discussed in Chapter 1. The purpose of this chapter is to discuss the
influences and relationships within each segment of the supply chain. Special attention is
given to the supply chain's response to forecasted demand changes. This discussion lays
the groundwork for analyzing the introduction of LNG into the supply chain.
As a note to the reader, unless otherwise cited, the information contributing to this
chapter's discussion is sourced from numerous publications, papers, and data presented
by the EIA.
Exploration
==
Production
Tran sttion
Consumption
Figure 18 - The Natural Gas Supply Chain in General Terms
Exploration
The purpose of exploration is to find new gas reservoirs and to refine the data
pertaining to existing ones. When a previously unknown gas deposit is discovered, it is
labeled as a "discovered natural gas resource." The end result of exploration is the
reclassification of resources to "proved reserves" or "proven reserves." As shown in
Figure 19, the steps in between are a refinement of information, which is done through
scientific processes. The process is geologically intense and consists primarily of seismic
38
surveying, deep wireline measurements, and exploratory drilling. Improvements to
seismic survey technology in the form of three dimensional (3D) measurement combined
with computerized data processing has made it possible to accurately chart and evaluate
large stretches of land. Comparing 3D seismic data over time (i.e. "4D") makes it
possible to analyze the movements of gas within the reservoir. This allows drillers to site
the ideal location for wells.
The exploration process is intent on promoting the most economically recoverable
resources to proven reserve status first. This requires a deep knowledge of likely supply
chain costs as well as influential factors from the economic environment such as demand
forecasts. Although it is the first step in the gas supply chain, the data interpretations
have tremendous influence on the future direction of the gas companies and their
consumers.
39
20
Total 41& Gas Resource Base
Undscove'ed
Resources
IT
DUsovered Resources
(Os and Gas In-place)
Economcmy
Unrecoveradle
Resources
ot
conomca y Recoveaie Resources
(UItnate Recovery)
Proved Ultimate
Recovery
Reserves
(W1 Proved)
Possible
Reserves
Probablie
Reseves
Proved
Reserves
Form
EIA-23
Includes
Proved
Non-Producing
Proved
Undeveloped
Cumulative
Producton
Proved
Developed
Producing
Proved
Developed
Non-Producing
Figure 19 - Classification Breakdown of Oil and Natural Gas Resources [EIA (5),20011
Although it is a necessary and expensive requirement for producing gas,
exploration has a cost that is not always traceable to a specific supply chain. For
instance, accurate exploration has been rampant in areas such as Texas, Oklahoma, and
Louisiana, but many of these discovered resources are still waiting to be exploited. In
these cases, profits from existing gas production are supporting exploration for new
resources. In addition, gas and oil are often found in the same reservoirs and often with
an uncertain estimate of commodity breakdown. Separating exploration costs is further
complicated because gas and oil follow separate supply chains. However, there are
notable exceptions to costing exploration occurring where researching and obtaining gas
resources is likely to push technology, costs, and risk beyond established limits.
Examples of potential resources requiring extensive exploration currently include deep
40
offshore, Alaskan, and Rocky Mountain resources. Due to the multitude of obstacles
involved in acquiring these gas resources, costs throughout the supply chain are certain to
be astronomical as well as unique.
To offer some perspective on the size of the domestic resource base, 2001 EIA
survey data estimates resources at 1,431 TCF with additional proven reserves of 183
TCF. Considering consumption estimates discussed earlier, it would take about 8 years
to consume the remaining proven reserves and 73 years to consume all available gas
resources. [EIA (5), 2001]
Production
As shown in Figure 20, production is the means of converting proven reserves to
a marketable product. Developmental drilling is the first step in the production stage.
Advances in exploration technology have increased the success rate for finding and
completing producible wells over unsuccessful drilling or "dry holes." Analyzing trends
in unsuccessful drilling by way of percent of wells drilled can be dubious across
commodities. Therefore, Figure 21 is presented to show the 50% decline in "dry holes"
since 1990 despite increased overall drilling. Upon completion of successful wells, gas is
extracted and refined in production facilities for consumer use. Although not shown in
Figure 20, the refining of extracted gas to market quality product typically yields a
significant amount of losses consisting of gas impurities (such as gas dioxides, helium,
hydrogen sulfide, and nitrogen) and liquid hydrocarbon constituents (such as propane,
ethane, and butane). In 2001, losses attributed to non-hydrocarbon gases amounted to
0.47 TCF. Also, losses attributed to liquid hydrocarbon constituents amounted to 0.93
TCF. [EIA (3), 2002]
41
&
Transportation
Distribution
Production
r
--------------
I
--- --- -- ---
-- --- --- -- --- -- --- --
By-Pass Storage
Exploration
Gas Production
Piein
yse
Proven Gas
ReservesExtraction
-----------------ReclassificatioInrdcint
IntroduStirngt
Strg
Storage
Natural Gas
Resources
Net Additions
Distribution
Deliveryi
------------------------------
|----
Consumption
Figure 20 -Simplified Stock and Flow Diagram of the US Domestic Natural Gas Supply Chain
42
-~1
35,000
30,000
-
"'v'""" Dry
Deve opment
r
Developmental Total
25,000
20,000
4
15,000
1 0,0001
5,000
0
1990
1992
1994
1996
1998
2000
2002
Figure 21 - New Developmental Wells Drilled per Year per Commodity [EIA]
Since the mid 1990's, gas producers have been moving toward 100% utilization of
existing production facilities. The FERC considers this to be an industry wide move
toward "just-in-time" delivery of natural gas as evidenced by Figure 22. With that said,
the current ability to increase production levels is almost completely dependent on the
number of drill rigs in service. Gas companies' ability to meet production requirements
by employing rigs is discussed later in this chapter.
43
IIIIIIIII---..................- . . . . . . ......------
80
U
---- -- -- -
-
III -I
.5
40
JOrn-95
Jar)-96
Jan-97
Jar,-99
-Gas
Production
J an-99
-Productive
Jaf)-0
JJ-0I
J0
,J-02
Capacity
Figure 22 - Graph of Domestic Natural Gas Production and Productive Capacity (BCF per day),
1995-2002 [FERC (1), 20031, [AGA]
Transportation and Distribution
Pipelines
Pipeline infrastructure and storage capacity are the major segments of this
category. Pipeline infrastructure consists of piping, valves, compressors, and
communications and measuring devices. Pipelines are divided into three scope-sensitive
groups, gathering lines, major pipelines, and distribution lines.
Gathering lines are minor pipelines that transfer gas from the wellhead to the
production facilities and/or from the production facilities to a pipeline station. Typically
gathering lines are proprietary and owned by drillers or gas producers because they are on
the property of their drilling or production facilities. However, in some cases, such as
where the gathering lines provide a public service to gas owners, the gathering lines are
44
--- -I-
-I-- _ - LL-_ .'I
I- ,-I,
- LL"LL
required under US law to be owned and operated by a pipeline company. Gathering lines
are typically considered part of the production stage.
Major pipelines are equivalent to interstate and intrastate highways in that they
are open to the public, require public approval for construction, and have published and
approved tariffs. Major pipelines connect producers with gas retailers. The Federal
Energy Regulatory Commission (FERC) regulates construction/expansion approval
authority and business practices for interstate as well as "qualifying" intrastate pipelines.
The U.S. Department of Transportation's Office of Pipeline Safety (OPS) holds and
regulates the standards of pipeline construction and safety procedures. All intrastate
pipelines are regulated to some extent by state authorities. Pipelines are required to be
owned by companies who maintain business practices no closer than an "arm's length"
away from gas owners. Pipeline companies are not permitted to own the gas transported
through their pipelines.
Distribution pipelines connect gas retailers with end use consumers. Ownership
and business practice requirements are no different on the distribution level; however,
regulation is maintained predominantly by state and local authorities. Pipeline companies
serving the retail market deal with different issues than those that serve the immediate
producers and the mass-transportation of natural gas. On a retail level, there are
predictable limits to what individual consumers will consume. Expansion of the retail
network entails laying relatively light pipe over short distances at a relatively low cost.
In many cases, local right-of-way may not be such a burdening issue because property
developers have already tackled it. A reliable quality of service is of the utmost
importance and incurs higher operational expenses from maintenance, communications,
45
metering, and general customer service. Pipeline companies owning and operating major
pipelines are faced with a growing demand from regional consumers as well as a
"spreading" network of producers. When utilization reaches 100%, pipeline companies
are faced with the dilemma of increasing capacity through pipeline replacement,
enlargement, or construction of new pipelines or of losing business. As gas companies
spread production to service smaller, more remote gas reservoirs, pipeline companies
expand their pipeline network to meet production. Since major pipelines enjoy large
economies of scale, service expenses are less of an issue; however, large pipeline projects
confront high construction costs (esp. offshore) and potentially large scale right-of-way
conflicts.
Storage
If not for storage infrastructure, the supply chain's response to increases in
consumer demand would quickly be faced with bottlenecks. As is discussed in greater
detail later in this chapter, storage acts as a buffer between production and consumption.
By making storage available to its customers, pipeline companies are able to reduce the
variation of major pipeline utilization. The opportunity to store natural gas during offpeak seasons offers gas owners the ability to purchase gas at lower prices in order to sell
it when the market is "hot." While storage is a necessary part of the supply chain, not
every cubic foot of gas passes through a storage facility.
Due to natural gas's physical characteristics of low density and high volatility, traditional
storage tanks used for propane and oil are not a viable option. In 2001, 86% of
deliverable natural gas was stored underground in natural geological reservoirs such as
those shown in Figure 23. These include depleted oil fields and gas reservoirs as well as
46
aquifers, caverns, vacated mines, and other open formations found underground.
Naturally occurring salt domes that are found in abundance in and along the Gulf of
Mexico are commonly hollowed into underground storage facilities. [EIA (4), 2002]
Source: PB-KBB, Inc.
A Salt Caverns
B Mnes
C Aquifers
D Depleted Reservoirs
E Hard-rock Caverns
Figure 23 - Types of Underground Storage Facilities [EIA (4), 2002]
Freestanding LNG storage facilities comprised the remaining 14% of deliverable
gas in 2001. Without having to rely on naturally occurring geological formations, LNG
storage can be located closer to end-use consumers. Due to the steep costs associated
with liquefaction and re-gasification, stored LNG is typically dispersed to customers
during peak consumption when the market price is justified. In other situations, LNG is
trucked to remote LNG storage facilities designed to service customers whose network is
small and isolated from or under serviced by feeder pipelines.
In closing the general discussion on natural gas transportation, Table 6 is
presented to illustrate the relationship between consumption, production, storage, and
pipeline utilization for the states in the consumption discussion. The importance of
storage is best shown in Michigan (MI) where deliverable storage exceeds the required
47
gas demand from interstate sources. Since interstate pipeline utilization is already near
100%, adequate storage is absolutely necessary in order to service Michigan's gas
consumers. [Tobin, 2001]
CA
FL
MA
Consumption
6,848
1,582
1,693
Production
1,055
16
Net Storage
624
MI
4,064
Needed
Utilization of Net
Interstate Pipeline
Capacity
NY
TX
4,225
11,892
17,413
-
496
-
49
-
-
2,020
-
560
-805
5,169
1,566
1,693
1,548
2,826
3,616
-4,716
74%
99%
88%
96%
70%
40%
Withdrawals
Net Interstate Gas
NJ
2,826
95%
I
Table 6- Interstate Pipeline Utilization per State During Their Peak Demand Month of 2000. All
measurements in MMCF/day [Tobin, 20011
Traditional Responses to Changes in Customer Demand
This section utilizes a quasi-system dynamics approach to analyzing the manner in which
the natural gas supply chain responds to changes in consumer demand. Since nearly all
forecasts predict overall gas consumption to continue to steadily increase, this section
only discusses responses to increases in demand.
Immediate
When responding to the consumers' immediate increase in demand, the supply
chain is actually responding to a forecast of roughly six months. Since immediate
changes in consumer demand are instigated by seasonal weather, a six-month time frame
is large enough to gain proper perspective through the capture of demand peaks and
valleys. Referencing
Figure 24, an Immediate Supply Shortfall is perceived as the difference between the
immediate forecast and all of the gas in route to the consumer. This gas is comprised of
stored, pipeline, and recently extracted gas that can be brought to the consumer in six
48
11111111111111111111111
-------. .....
............................
........
11 1 1.............
months time. Should the supply chain have difficulty meeting the forecasted or even
momentary demand, the market adjusts gas prices quickly. If there is adequate time to
handle the perceived shortfall, the supply chain manipulates storage levels and pipeline
utilization. "Adequate time" may be less than a month in regions close to production or
around two months in heavily populated regions such as the Northeast.
Importing natural gas is also a response option. Increasing imports from Canada
is subject to the same issues as increasing supply from domestic storage and producers
and is therefore limited. Increasing LNG imports by utilizing more of the existing LNG
supply chain capacity or engaging in spot contracts is also an option.
1 -1
Extraction
GasnPieieSsm
Production
Pipeline System
Canadi
LNG
Imports
Impo s
P~roven Gas
Reserves
Reclassification
Nasure
Resources
Immediate Suppl
Shortfall
+
Distribution
D : Delivery
6 m onths
+
Demand Forecas
Net Additions
+<
+
Market Gas
Price
Net Price Increase
Fractional Influence of
Immediate Supply
Consumption
Figure 24 - Stock and Flow Diagram of the Natural Gas Supply Chain's Response to Immediate
Changes in Demand
49
Short-Term
As per Figure 25, the short-term response is determined from a perceived Shortterm Supply Shortfall that is based on a six to eighteen month demand forecast. This
forecast should not hold too many demand surprises for supply chain participants.
Instead, the supply chain focuses particularly on production as the means of meeting
demand needs for the period following next demand season. Since the typical lag
between the times drilling operations are mustered and a new well's gas reaches
consumers is six to eighteen months, producers evaluate the need for new wells. The
evaluation is based mostly on the productivity status of existing wells and the progress of
still unproductive drilling projects. Typically, new drilling projects that are undertaken to
satisfy this shortfall have been well explored, are often extensions of existing projects, do
not require exceptional capital outlays, contain "quick-to-market" gas, and are generally
not risky. Canadian and/or LNG import options similar in nature to the immediate
response are also viable.
50
-
Pipeline System
Gas Production
Canadian
Imports
Extraction
Prveas11
LNG
Imports
Injection to
Storage
+
Drilling
By-Pass
Storage
+at
Reserves
+
Reclassification
torage
D e n FS
Short-term Supp
Shortfall
Natural Gas
Resources
Distribution
Demand Forecas
6-18 months +
TNet
Additions
+o
Net Price Inctease
Delivery
sFractionalInfluence of
Short-term Supply
Consumption
MPrice~a
Figure 25 - Stock and Flow Diagram of the Natural Gas Supply Chain's Response to Short-term
Forecasted Changes in Demand
The market adjusts natural gas prices based on the perceived size of the shortfall.
Since the shortfall is forecasted to be six to eighteen months away, the effect on current
gas prices through FractionalInfluence ofShort-term Supply is much less than an
immediate shortfall; however, the short-term shortfall affects the price of natural gas
derivatives more than the immediate shortfall.
Mid-Term
As per Figure 26, the mid-term response is determined from a perceived Mid-term
Supply Shortfall that is based on an eighteen month to five-year demand forecast. In
order to meet the demand at this later time, the supply chain focuses on pipeline
51
I1- -11 A
................
.. ........
- -
1. 1.
1.....
. ........
- -
infrastructure and available production. In this window of time, future new consumers
are identified such as power plants and major industrial facilities. Pipeline companies
begin the process of meeting these new customers' needs. With regards to production,
producers are willing to engage in larger and riskier drilling projects where most of the
exploration has already been done. For example, offshore drilling projects, short of those
located in deep waters, are feasible at this point because there is adequate leadtime for
gathering investment funds and construction. Expansion of existing LNG supply chains
in the form of new ships and export trains are also options at this point.
A mid-term supply shortfall has a limited effect on gas prices. In fact, the
shortfall would have to be very serious to have virtually any effect.
By-Pass
Storage
Gas
Production
Pipeline System
to
PecassGciInjection
+pStorage
vers
res ReservesExtraction
Storage
Drilling
Rate
Reclassificatio4
Natural Gas
ResuresMid-term
+Distribution
Supply_1
Shortfall
D -Delivery
Net Additions
Demand Forecast,
18-72 months
+
+
Gas Infrastructure
Improvements &
Import Plans
Fractional Influence of
Mid-term Supply
+
M ar e eG a s
Net Price Increas
r
+
Consumption
-1
Figure 26 - Stock and Flow Diagram of the Natural Gas Supply Chain's Response to Mid-term
Forecasted Changes in Demand
52
- -
-
__
-
IA
Long-Term
As per Figure 27, the long-term response is determined from a perceived Longterm Future Supply Shortfall that is based on a demand forecast over five years in the
future. The major concern facing the supply chain is the status of the natural gas resource
and proven reserve base. Enormous exploration budgets are allocated to determine the
specifics of reservoirs required for producing gas in the distant future. This is also the
time when the largest projects are considered for future production. Projects such as deep
offshore, mountainous, and arctic drilling and entire LNG supply chains are evaluated
because enormous exploration, development, and production investments may be tied
specifically to individual projects. In order to satisfy growing demand locally or
regionally, pipeline and storage infrastructure are evaluated to determine their ability to
safely meet the requirement. This results in added capacity to existing growing networks
and new infrastructure to unconnected or potential networks of consumers. Pipeline
companies also work to identify opportunities for cost savings where pipelines and
delivery systems and networks can be streamlined.
Unless it is catastrophic in magnitude, a long-term supply shortfall has little to no
effect on natural gas prices.
53
GasReipslen
Production ductioneSy
Injection to
Storage
Extractioni
Reclass/Refine
Gas Data
Natural Ga
Resources
Storage
Development of Major
Production Projects
Explorati
Budgets
Distribution
~Net
Budge
Net
Additions
+
+
i
,
Proven Gs
ReseDrs
Ste
Dlvr
I
Long-term Future
_ Supply Shortfall
Consumption
Gas Infrastructure
Investments & Import
Plans
Demand Forecast.
5+ years
+
Resource & Reserve
Forecasts
+
Figure 27 - Stock and Flow Diagram of the Natural Gas Supply Chain's Response to Long-term
Forecasted Changes in Demand
54
Chapter 4 - Supply and Demand Portfolios of Major Natural Gas
Consuming Countries
Although Chapters 2 and 3 only specifically cover the United States, the factors
affecting US consumption are similar in nature to those present in other countries. A
major difference between the US and most other major gas consuming nations is that the
US has enjoyed an abundant supply of domestic gas as well as other energy sources. The
purpose of this chapter is to summarize the gas consumption and supply portfolios of
selected countries. Focus is given to countries that are being evaluated for the import of
LNG or currently receive LNG imports.
Belgium
Figure 28 shows that Belgium's gas market is well diversified because power
generation and industrial customers consume the majority of gas. Despite their steady
consumption, residential consumption is still high enough to make peak monthly
consumption swings as high as 2.3 times the lowest monthly consumption. As shown in
Figure 29, the lack of storage combined with a non-existent indigenous gas supply
contributes to a cyclic need for imported gas.
1%
39
12%
Commerce - Public services
SC
*
Residential
*
Power generation
*
[ndustry
0 0the rs
23%
Figure 28 - Breakdown of Natural Gas Consumption in Belgium by Sector, 2000 [IEA (1), 20021
55
2500
2000
11111(
500
10
-500
---
Stock
changes
-Gross
inland consumpticin
-
imprt
'--Iota]
Figure 29 - Trends in Monthly Natural Gas Consumption, Imports, and Storage Changes in Belgium,
1998-2000 [IEA (1), 2002]
China
China has tremendous natural gas reserves estimated at 53 trillion cubic feet;
however, the mountainous terrain, distance from major population centers, and lack of
interior development in general has made exploiting gas reserves difficult. The
population's potential for consumption in all sectors is encouraging foreign direct
investment in gas pipelines and regasification terminals. As of 2001, natural gas
comprised less than 3% of the country's fuel needs. In 2001, China consumed 1 trillion
cubic feet of natural gas. The EIA predicts that consumption will grow to 2.3 trillion
cubic feet in 2010 and 4.5 in 2020. [EIA (2), 2003]
France
France's natural gas consumption profile is not well diversified. As evidenced by
Figure 30, consumption is virtually split between cyclic residential and commercial
consumers and consistent industrial consumers. This creates tremendous seasonal swings
in consumption which can vary by a factor of 4 as per Figure 31. Although their
56
consumption profile is disadvantageous, their aggressive use of available storage enables
them to import a steady amount as shown in Figure 32.
2VK%
U
41
Crnerc-- Public services
IRsidenrtial
PPower geniration
Ilndustry
LJ U
27%-
Figure 30 - Breakdown of Natural Gas Consumption in France by Sector, 2000 [IEA (1), 20021
mcrn
-7000
6000
5000
4000
3000
2000
1000
0-
0 C
C
14
4e
p
1 Residential, cornmercial and small industry
* tndustry
Figure 31 - Monthly Consumption Patterns of France's Major Natural Gas Consumers, 1998-2000
[IEA (1), 20021
57
main
80006000400020001
p,
o
-2000
-4000
---
StoA~ ciiangc-,
-
(1io inland
corimpci
Pr( uci
~
-
--
tal irnporis
Figure 32 - Trends in Monthly Natural Gas Consumption, Production, Imports, and Storage
Changes in France, 1998-2000 [IEA (1), 20021
In 2001, France consumed 1.5 trillion cubic feet of natural gas. The EIA predicts
that consumption will grow to 1.6 trillion cubic feet in 2010 and 2.6 in 2020. [EIA (2),
2003]
Greece
As it accounts for only 6% of its energy needs, Greece is still developing natural
gas infrastructure. The push for more efficient, cleaner burning power plants contributed
to gas development in the area of power generation which comprises the great majority of
Greek consumption (see Figure 33). However, the nature of the consumer is such that
consumption is rather steady that is until air conditioning becomes more predominant.
Figure 34 illustrates the growing use of gas and the lack of storage changes available to
handle consumption fluctuations. Currently, storage facilities are limited to LNG tanks at
the import terminal.
58
'
Commerce - Public ervices
N ResidenCial
E Power generation
0
Industry
0 Othiers
76%
Figure 33 - Breakdown of Natural Gas Consumption in Greece by Sector, 2000 [IEA (1), 2002]
ifcm
r
250
200
150o
100
-0-
1;
Cb0-----------------------------------------------*w
$C6
- - - Stock changes
4
Gross inland consumption
"N
-
Production
-
-
50
4-&"r
Total imports
Figure 34 - Trends in Monthly Natural Gas Consumption, Production, and Storage Changes in
Greece, 1998-2000 [IEA (1), 20021
India
India is strongly pursuing gas infrastructure development as it continues to
develop electrical power delivery to the nation's enormous population; however, energy
development in general has gone much slower than expected. In 2001, India consumed
0.8 trillion cubic feet of natural gas despite advanced EIA forecasts of 2.7 trillion cubic
feet per year. The EIA predicts that consumption will grow to 1.8 trillion cubic feet in
2010 and 3.1 in 2020. [EIA (2), 2003]
Italy
Italy has a well developed gas market that comprises 34% of domestic energy
requirements. Although consumption is split evenly between power generation,
59
industrial, and residential consumers (see Figure 35), Italy still experiences large
fluctuations in consumption. The seasonal peaks are about 2.7 times higher than the
lowest consumption periods as illustrated by Figure 36. Since Italy has storage capable
of handling 50% of the annual residential consumption, fluctuations are handled through
inventory adjustments and variations to import volumes (see Figure 37).
32Wj
1 Power genertio n
M
IndUStry
(Others
Figure 35 - Breakdown of Natural Gas Consumption in Italy by Sector, 2000 [IEA (1), 20021
mcm
10000
9000
8000
.000
6000
5000
4000
2000
-
3000
1000
0
I Pr wcr gencration
E [ndustrial
U
Residential - Commercial
Figure 36 - Monthly Consumption Patterns of Italy's Major Natural Gas Consumers, 1998-2000
[ILEA (1), 20021
60
TICI11
12001-1
10000
t
4000
40(10
-pW#
F
-60
0
0
go
M
on EP 41
M
I
23000#
-4000C91
'N'
-
Stcs -I.iIg~ (arLm Ulrlid C(Il
tjfril
-
liuto
-
-total
imlport's-
-
Figure 37 - Trends in Monthly Natural Gas Consumption, Production, Imports, and Storage
Changes in Italy, 1998-2000 IIEA (1), 2002]
In 2001, Italy consumed 2.5 trillion cubic feet of natural gas. The EIA predicts
that consumption will grow to 2.7 trillion cubic feet in 2010 and 3.4 in 2020. [EIA (2),
2003]
Japan
Japan has a sophisticated natural gas market that relies almost completely on
imported LNG. Although Japan's consumer profile is terribly one-sided in favor of
power generation (see Figure 38), the annual consumption profile is actually quite stable
as per Figure 39. The reason for this is the heavy utilization of electricity for heating and
cooling purposes. Stability is such that the peak seasonal consumption is only 40%
higher than the lowest month's consumption. As is discussed in later chapters, Japan's
stable profile enables their dependence on LNG. Stability also permits minimal use of
storage facilities which are entirely above ground LNG tanks at regasification terminals.
Figure 40 shows how consumption and imports are practically equal. Japan protects
61
itself against supply risk through a diversified portfolio of LNG suppliers. Eight
countries are engaged in LNG trades with Japan, and they service 22 regasification
terminals throughout the country.
0 Commerce - Public servitcs
*
Rusidcntial
*
Power generation
*
Industry
0 Others
Figure 38 - Breakdown of Natural Gas Consumption in Japan by Sector, 2000 [IEA (1), 20021
ncm
-
-
6000
S000
6000
E IPower gencration
.3000
UCity gas
2000
1000 *
ohdustrial
0
Figure 39 - Monthly Consumption Patterns of Japan's Major Natural Gas Consumers, 1998-2000
[IEA (1), 2002]
incm
9000
6000
e%
-
ou%.
4
4000)
2000
-2000-
- - - StOck changL
----
Css inland
anSumptin1s
Production
-
-
- Tbtal
imports
Figure 40 - Trends in Monthly Natural Gas Consumption, Production, Imports, and Storage
Changes in Japan, 1998-2000 [IEA (1), 20021
62
In 2001, Japan consumed 2.8 trillion cubic feet of natural gas. The EIA predicts
that consumption will grow to 3.2 trillion cubic feet in 2010 and 3.4 in 2020. [EIA (2),
2003]
Korea
Similar to Japan, Korea's natural gas supply is composed of 100% imported LNG.
However, unlike Japan, Korea suffers from consumption fluctuations despite a better
diversified consumer profile (see Figure 41). Seasonal residential consumption drives
peak consumption 2.8 times higher than the lowest month's consumption as per Figure
42. Similar to Japan, Korea does not have underground storage infrastructure and
therefore relies on LNG tanks storage at the country's two regasification terminals. With
little flexibility for storage, LNG shipments must practically match consumption as
shown in Figure 43. With such fluctuations, Korean gas buyers must make contractual
concessions (see the chapter on contracts); however, this is facilitated by spreading
supply across 5 different long-term LNG suppliers and at least one spot supplier.
cI
2
1
U mCommercc - Public services
M Rsintial
M
E
Power genlerati0on
Idustry
Figure 41 - Breakdown of Natural Gas Consumption in Korea by Sector, 2000 [ILEA (1), 20021
63
nic ni
2500
2000
1 500
1000
0 R-osidential
500
B Commercial
0 Industry
13Pwer ecririltion
Figure 42 - Monthly Consumption Patterns of Korea's Major Natural Gas Consumers, 1998-2000
[IEA (1), 20021
m cm
3000
2500
2000
500
1998-2000 [IEA (1), 2002]
In 2001, Korea consumed 0.7 trillion cubic feet of natural gas. The EIA predicts
that consumption will grow to 1.2 trillion cubic feet in 2010 and 1.7 in 2020. [EIA (2),
2003]
Mexico
Natural gas consumption in Mexico is limited to power generation and industrial
uses and comprises 24% of the country's fossil fuel needs. Adequate distribution
infrastructure to residents, especially in the northern half of Mexico, does not exist.
64
Proven reserves measured 8.8 trillion cubic feet in 2000, but due to poor pipeline
infrastructure, domestic production is limited to southern Mexico. Imports from the
United States supply northern Mexico and comprise 8% of the country's total annual
supply. Mexico is looking at LNG imports as a means of ensuring its long-term supply
portfolio and as a means of reducing high-priced US imports. [EIA]
In 2001, Mexico consumed 1.4 trillion cubic feet of natural gas. The EIA
predicts that consumption will grow to 2.4 trillion cubic feet in 2010 and 4.3 in 2020.
[EIA (2), 2003]
Portugal
Driven by the power generation sector (see Figure 44), Portugal is further
developing its small natural gas infrastructure which is supplied only by pipeline from
Algeria and LNG from Spanish terminals. Since power generation provides for cooling,
seasonal fluctuations are 2 times higher than the year's lowest consumption month.
Consumption fluctuations and the rate of gas development are illustrated in Figure 45.
Portugal has no storage infrastructure.
U
Commerce - Public
servcs
SResiden tial
Pciver g1ernron
*
Industr
Fbther,
Figure 44 - Breakdown of Natural Gas Consumption in Portugal by Sector, 2000 [EEA (1), 2002]
65
mcm
300-
250200
100
0-A50
---
S tck changes
Gross inland consumption
-
Total imports
Figure 45 - Trends in Monthly Natural Gas Consumption, Imports, and Storage Changes in
Portugal, 1998-2000 [TEA (1), 2002]
In 2001, Portugal consumed 0.08 trillion cubic feet of natural gas. Portugal's
Ministry of Economy expects consumption to grow to 0.2 trillion cubic feet per year in
2010. [IEA (1), 2002]
Spain
Spain's gas market is dominated by industrial consumers at 63% of annual gas
consumption (see Figure 46). The small seasonal presence of residential and commercial
buyers comprising 17% of consumption is able to generate peak consumption months that
are twice that of the lowest consumption month. Despite limited storage capabilities, a
buffer between consumption and imports (see Figure 47) is made possible through take
flexibility in the LNG contracts (discussed more in later chapters). LNG comprises 50%
of imports and is supplied from at least five different ventures.
2%~4 %i.~
*
Commerce - Public services
Residenrial
Power gceration
R
63%
!!_1__
0
Industry
0
Others
Figure 46 - Breakdown of Natural Gas Consumption in Spain by Sector, 2000 [LEA (1), 20021
66
incin
2000-
1000-N
m
0-
---
Stuck cha nges
---
G
ss
Inak nd collRIMumptOn
-
Production - --
Otal imnports
Figure 47 - Trends in Monthly Natural Gas Consumption, Production, Imports, and Storage
Changes in Spain, 1998-2000 [IEA (1), 20021
In 2000, Spain consumed 0.6 trillion cubic feet of natural gas. Based on
supply projects under construction, Eurogas Corporation predicts that consumption will
grow to 1.3 trillion cubic feet in 2010 and 1.8 in 2020. [Eurogas, 2002]
Taiwan
Natural gas represents 6% of the island's energy consumption where most is used
in power generation and some for industrial purposes. Taiwan has no indigenous
reserves and therefore imports LNG from Indonesia and Malaysia to one terminal. In
2001, Taiwan consumed 0.23 trillion cubic feet of natural gas. The EIA estimates that
consumption will grow to 0.7 trillion cubic feet in 2010. [EIA]
Thailand
Natural gas represents 25% of the country's energy consumption where most is
used in power generation. In fact, all oil-fired power plants have been converted to gas.
Thailand has significant gas reserves, both onshore and offshore, measured at over 12
trillion cubic feet; however, physical and financial difficulty in exploiting the reserves
has prompted Thailand to seek pipeline and LNG import options. In 2001, Thailand
67
consumed 0.66 trillion cubic feet of natural gas. The EIA estimates a 6% growth rate
which should increase consumption to 1.1 trillion cubic feet in 2010 and 2.0 in 2020.
[EIA]
United Kingdom
The UK has a strong natural gas market that comprises 38% of all energy
requirements. The consumer profile is somewhat diversified (see Figure 48) because
much of the power generation is stable base load generation. This point is made in
Figure 49 where the consumption profile has peaks that are only twice as high as the
lowest consumption quarters. Less than 2% of total gas supplies are imported because of
adequate production from North Sea gas fields. Since the fields are close to the market
and can produce flexibly, storage requirements are relatively small (see Figure 50). The
UK's Reserve-to-Production ratio is only 7 which means that current production rates
could only continue for 7 more years. This is a major issue for a country with limited
import infrastructure and heavy dependence on natural gas.
9%
2CP*4
9%
U
Cornmerce - PuIhbic serv ices
*
lksidenitial
3
I0wer
generation
*
[ndustry
O
Others 1)
29%
Figure 48 - Breakdown of Natural Gas Consumption in the United Kingdom by Sector, 2000 [IEA
(1), 20021
68
G;wh
400000
350000
300000
250000
200000
I 0000
100000
50000
0
5~
E Rideta
SOthers
4
C
emmerciaI
M Industry
0 Electricity gLeneraton
Figure 49 - Quarterly Consumption Patterns of the United Kingdom's Major Natural Gas
Consumers, 1998-2000 [IEA (1), 20021
mcm
14000
12000
I 0000'
8000
6000
4000'
2000
1z
0
-m
-2000
- - -
Stock changes
-
Gs
inland onsumption
Productin
-
-
Toal
imports
Figure 50 - Trends in Monthly Natural Gas Consumption, Production, Imports, and Storage
Changes in the United Kingdom, 1998-2000 [IEA (1), 20021
In 2001, the UK consumed 3.3 trillion cubic feet of natural gas. The EIA predicts
that consumption will grow to 3.7 trillion cubic feet in 2010 and 4.8 in 2020. [EIA (2),
2003]
69
70
Chapter 5 - The Liquefied Natural Gas Supply Chain
The purpose of this chapter is to briefly describe the segments of the LNG supply
chain: feedgas acquisition, liquefaction plant and terminal, LNG tankers, and
regasification plant and terminal. Instead of delving into great detail about the specific
processes and equipments involved in each segment, the discussion focuses on the issues
and sensitivities that have the greatest effect on required capital, operating expense, and
project risk.
Feedgas
Like supply chains based on traditional pipeline gas, the LNG supply chain begins
in gas and oil fields located deep in the earth. Therefore, traditional means of extraction
such as those discussed in Chapter 3 are utilized. The raw gas is piped to the liquefaction
plant for processing. The quality and composition of the feedgas influences decisions
regarding the design of the liquefaction plant and/or the possible target markets. Just as
associated natural gas is an issue for crude oil producers, byproducts and contaminants
are a problem for natural gas producers. Byproducts such as hydrocarbon condensates
and liquid petroleum gases (i.e. propane and butane) are separated from the feedgas early
in the liquefaction process. They are removed for several reasons. Removing the heavier
hydrocarbons brings efficiency to the liquefaction process. LPG's are used as a
refrigerant within the liquefaction process. Feedgas composition varies from gas field to
gas field, and therefore regional markets have set standards for gas composition that
reflect the typical composition of regional gas supplies. Lastly, the LPG and condensate
byproducts are marketable products; however, this varies heavily from region to region.
71
The extent and manner to which these byproducts are dealt with influence the costs and
revenues involved with the project. [Greenwald, 1998]
Typical contaminants include carbon dioxide, water, hydrogen sulfide, and
mercury. These must be removed before liquefaction because they contribute to the rapid
and, in some cases, immediate degradation of the equipment involved in liquefaction.
Not only must equipment and processes be designed for the removal of contaminants, but
disposal must also be arranged that is satisfactory with the host government. An
excellent example of the problems presented by contaminants is the lack of development
of the Natuna gas field in Indonesia. Natuna is estimated to hold 46 trillion cubic feet of
recoverable reserves but is estimated to contain 200 trillion cubic feet of carbon dioxide.
Despite the field's close proximity to Japanese and Korean LNG markets, the massive
amounts of carbon dioxide continue to make the costs associated with liquefaction
prohibitive. [Drewry, 2001]
Lastly, gas reserves must be large enough to supply the LNG project for the
intended period. For instance a small LNG plant producing 1mmtpa requires at least 51
billion cubic feet of gas per year or over 1 trillion cubic feet over 20 years. This may be a
major issue limiting the viability of exporting small gas reserves in LNG form. Utilizing
several small gas sources within a single project may improve long-term viability
although different types of gas filtering may be required.
Liquefaction Plants
Liquefaction plants are comprised of many stages including feedgas filtering,
liquefaction, storage, and cargo loading. Issues influencing the filtering of feedgas were
discussed in the previous section. The liquefaction stage involves cooling the filtered
72
feedgas to -253'F (-161'C) where it becomes a liquid. This is done using a licensed
process that resembles the refrigeration process in a typical refrigerator. ConocoPhillips
(formerly the Phillips Company) and Air Products and Chemicals Inc. own the rights to
the two most commonly used processes. The plant's complete series of equipment and
processes required to produce LNG from feedgas is referred to as a train. To allow for
maintenance and to avoid the full consequences of minor failures, liquefaction plants are
typically designed into multiple trains. Building multiple trains requires a greater initial
investment and larger operating costs but offers the potential for early revenues and
reduced operating risk. [Greenwald, 1998]
When building a plant with multiple trains, there are benefits to constructing the
trains in "series"(as opposed to in parallel). Most liquefaction plants do not begin
producing LNG at rated capacity when initially commissioned, but instead they
experience a "build up" or "ramp up" period where production gradually increases to
rated capacity. There are several possible reasons for this situation.
" Due to drilling and well production limitations, feedgas to the liquefaction plant
may not immediately reach capacity levels.
" Construction schedules may focus on one liquefaction train at a time. The same
can be said for one storage tank or loading pier.
" Equipment for condensate and by-product handling can limit production and may
be left for the end of construction.
*
Project financial requirements may necessitate early income from sales.
73
*
The fleet of newly built ships required to service the new trade will not be ready
all at once without bearing significant cost. A "build up" period may coincide
well with vessel deliveries.
The choice of storage facilities weighs significantly on a plant's overall costs.
Storage capacity is dictated by the plant's output, shipping frequency, tanker capacity,
and an inventory safety factor. Storage tank design is also a factor. In general, the tank
may be a single, double, or full containment design. The single containment tank is a
well-insulated, single-skin structure as opposed to the full containment design which is
double-skinned with a concrete outer wall. Although the full containment tank is airtight,
the single and double containment tanks are not. In case a spill does occur, all tanks are
surrounded by a low earthen or concrete dam known as a bund. The bund's required
dimensions, as dictated by designers and local regulations, change significantly with the
choice of containment system. A single containment tank requires the most area for the
bund and the greatest distance between tanks. [Greenwald, 1998]
Loading options are fairly limited but conservative design decisions may reduce
risks of delay while initially costing more. One loading pier is required; however, a
second loading pier may reduce vessel delay. In addition, investment in construction of a
breakwater may reduce delays due to weather. In both cases, detailed simulations can be
performed during project analysis to determine the best course of action.
LNG Tankers
On a relatively long distance supply chain, shipping capital costs are likely to be
the largest expense. Tanker designs are fairly standard; however, there are options that
influence capital and operating costs. Similar to most shipping trades, increasing vessel
74
capacity generates the greatest economies of scale. LNG tankers range in capacity from
19,000 to 145,000 cubic meters with tankers between 125,000 and 135,000 cubic meters
comprising over 80% of the world fleet. It is rumored that tanker capacities over 200,000
cubic meters are being considered for new projects. Operational factors have limited the
foray into larger ships. Restrictions on vessel draft and length have limited access to
existing and potential ports. The development of an LNG spot trade might also prevent
construction of larger ships if they would not have access to the spot cargos.
Since LNG must be kept below its dew point to prevent flashing back into its gas
phase, the method of containing the cargo during transport is critical. This flashing is
referred to as boil-off and special cargo containment systems attempt to minimize it.
Currently, there are four different licensed designs that dominate the existing LNG fleet
as well as those under construction. The four containment systems and their proprietors
are listed in Table 7. The choice of system generally has little effect on the newbuilding
price, and each system permits a comparable amount of boil-off. The acceptable level of
boil-off as specified by the newbuilding contract can be decreased by adding more
insulation to the containment system. Of course, this comes at additional cost. A
significant difference between systems is that membrane designs require a heel that is
about 30% of that required by Type 'B' designs. Heel is LNG that remains in the cargo
tanks for the return trip to the liquefaction plants and is intended to keep the tanks cool.
[Greenwald, 1 998]
Table 7 - LNG Tanker Cargo Containment Systems [Greenwald, 19981
Cargo Containment System
Self-Supporting Prismatic Type 'B'
Dual Membrane
Single Membrane
Licensor
Conch/IHI
Gaz Transport
Technigaz
Self-Supporting Spherical Type 'B'
Kvaerner Moss
75
Boil-off must be removed from the cargo tanks to keep the LNG cool and to
prevent an explosive situation. In nearly all existing tanker designs, boil-off is used for
fuel in the ship's steam plant. As opposed to the rest of the merchant shipping fleet, even
new LNG tankers are designed with steam plants for propulsion and electrical generation.
The steam plant has been required in order to burn the boil-off. Currently, technology is
working to free the LNG tanker of the need for steam plants. For instance, one of the
world's leading diesel engine manufacturers, Wartsila, is installing their first dual-fuel
marine engines aboard LNG tankers currently under construction. [Wartsila] Instead of
burning the boil-off, several engineering and refrigeration companies are designing
onboard re-liquefaction plants. This would also allow for a diesel engine propulsion
plant. Since natural gas prices have been unusually high for the last four years and often
above oil prices, onboard re-liquefaction permits maximum revenue from a vessel's cargo
and the ability to burn only the low cost bunker fuel for the ship's power. Unfortunately,
the costs and risks involved in both concepts have delayed their acceptance within the
LNG shipping community.
Regasification Plants
There are four major aspects to regasification plants: unloading, storage,
regasification, and security. The unloading aspect is similar to the loading aspect of
liquefaction plants. This includes the optimal combination of unloading piers or berths
and perhaps a breakwater as determined by a sophisticated transportation simulation.
LNG storage considerations are also similar to those of the liquefaction plant.
The major difference is "location." Most liquefaction terminals are located away from
population centers and sometimes in undesirable locations where real estate is
76
inexpensive. The supply chain stands to profit the most on higher gas prices which
encourages the regasification plant to be located near populated consuming centers.
Terminals in Japan, Boston, Massachusetts, and Savannah, Georgia are examples of such
locations. In these areas, waterfront real estate is at a premium. The price and
availability of land certainly affects the choice of storage containment system and the
number of ship berths.
Regasification needs at the plant fail in comparison to liquefaction
equipment needs at the export facility. Regasification takes place in two ways. LNG in
storage is constantly producing boil-off. Large vaporizers are sized to meet the peak
needs of local gas consumers. The plant vaporizes the LNG in a manner such that the
quality and pressure is suitable for downstream consumption. [Marcus, 1977]
Security is an issue that touches all other plant aspects. The author believes than
an entire paper can and should be devoted to the subject of LNG tanker and terminal
safety. To date regarding the handling of combustible cargo, LNG tankers and terminals
have enjoyed a flawless safety record. In response to the events of September 11, 2001,
ports and terminals have responded to the public's desire for greater security around
terminals and around tankers operating in confined, populated waterways. Heightened
security concerns have contributed to the public's general disapproval of locating an
LNG terminal anywhere near population centers. Large sums of money used to sway
public opinion and judicially defend the project's viability are required from the start for
an LNG venture to attempt such a feat. Offshore terminals are currently being considered
to mitigate security concerns and other general concerns of society.
77
A recent example of public opinion negating a project is the proposed import
facility by Shell and Bechtel at Mare Island in Vallejo, California. Both companies
offered many concessions in the form of subsidies to local organizations and government,
public specification of pollution and aesthetic requirements, and subsidization of
numerous third party project evaluations. The public's overwhelming support against the
project ultimately caused Shell and Bechtel to abandon the project. This event has
contributed to the coining of the term "BANANA" ("build absolutely nothing anywhere
near anything"). The Vallejo News has covered all aspects of the proposed project
thoroughly, and a detailed summary is available on their website at
www.vallejonews.com.
78
Chapter 6 - The Effect of Contract Terms on Risk and Project
Feasibility
From the concept engineering to the daily operation, LNG projects are surrounded
by risk. The sheer magnitude of the required investment, the volatility of the natural gas
market, and the global nature of the project are major factors that contribute to the unique
and unprecedented risks inherent in LNG projects. The risks are managed by a
complicated web of contracts, agreements, and trust as indicated by the flow chart on the
next page. The cornerstone of an LNG project is the Sale and Purchase Agreement
(SPA). The SPA establishes the terms and conditions by which the buyer and seller will
cooperate throughout the life of the project. The contract covers every specific aspect of
the buyer-seller relationship including, but not limited to: contract effective, plant startup, and termination dates, terms of delivery, annual and monthly delivery volumes,
schedule of deliveries, demurrage, gas quality, methods of payment, methods for dispute
resolution, applicable law, and most importantly the gas pricing structure. This chapter
discusses how some of these aspects affect the feasibility of the LNG project. Due to
their paramount significance, the discussion focuses on price structure, terms of delivery,
and volume agreements.
Addressing all facets of LNG contracts and risk is a thesis in itself; however, the
author invites the reader to read Greenwald's Liquefied Natural Gas: Developing and
FinancingInternationalEnergy Projects for detailed discussions or read Parker Larson's
thesis The Technology and Economic Feasibilityof Offshore Liquefied Natural Gas
Receiving Terminals in the UnitedStates for a thorough summary of countermeasures to
risk. [Greenwald, 1998], [Larson, 2003]
79
Port
Authorities
Neighboring
LNG Project
Insurance
Company
Capital
Expenditure
Onshore FEED
Offsh ore FEEDci/
Cooperation and
Coordination Agreement
71
. ~i
Parent
rance Policies
Company
Insurance Reports
Port Usage Agreement
100%
Guarantee
- EPC Contracts
- Drilling Sources
Agreements
- Drilling
.
'
-
Sources
- SFC
Techn ical Reports
- Finai ncing
Agree ments
LNG
Project Services Agreement
Joint Venture Agreement
-
Debt
Debt
Financing
Private
Sponsor
Servic
State
Enterprise
Sponsor
Lease for
Sale and Purchase
Project Site
Agreement (SPA)
Offshore
Bank
Accounts
Revenues
,
100%
LNG Buyer
Development and
Fiscal Agreement
or Production
Sharing Contract
80
Guarantee
Host
Government
I
Pricing Structure
Results in Chapter 8 show that the selling price of imported gas is critical to the
feasibility of the LNG project. Therefore, well before project capital is allocated, the
pricing structure must be agreed upon between buyer and seller within the SPA. Because
LNG is not yet a publicly traded commodity on financial exchanges, pricing structures
are agreed upon based on a combination of arbitrage opportunities, project risk, and
negotiating strength.
The concept of basing part of the price of LNG on arbitrage opportunities is based
on the premise that LNG is merely another energy source with a commonly accepted,
measurable heating value that must be delivered to a specified location. For example, in
a perfectly competitive market, the price an energy buyer is willing to pay for natural gas
at the "tailgate" or output point of a regasification plant should be equal to the delivered
price to that location of an equivalent amount of a prevailing fuel such as oil or coal.
Therefore, the LNG pricing structure can be tied to an index of publicly traded fuel
commodities.
In his 2002 IMF paper, Okogu explains the gas pricing formula commonly used in
LNG SPA's. The index based formula is as follows:
Pt = z*Ki
(1)
where:
P is the price of gas at time t
7r
is the "pass through" coefficient negotiated under the contract
K is the price of the competing fuel at the agreed time
i
=
0, 1, 2... .(usually measured in months)
81
K refers to the publicly traded fuel commodities mentioned above at the time of delivery.
ir
reflects the heating value conversion from the fuel index (i.e. barrels of oil) as well as
other factors that are discussed later in this section. Okogu provides the example of the
index commonly used for Japanese LNG imports, the JCC or Japanese Crude Cocktail.
The example is as follows:
JCC, = S + p*QLC,
(2)
where during period t:
JCC is the price per barrel of a cocktail of crude oils imported into Japan
QLC is based on the forecast price per barrel of the OPEC basket
p is the historic market correlation between prices of the OPEC basket and the
comparable basket in Japan. (Okogu's example suggests 0.95297774)
S represents shipping costs and other required price mark-ups (normalized per
barrel) for delivery to Japan (Okogu's example suggests 2.2252145)
In these examples, JCC is substituted for K in formula (1). While the oil based indexes
have been commonly used since the beginning of LNG SPA's, Okogu and the IEEJ's
Morita suggest changes have occurred and more is coming. [Okogu, 2002], [Morita,
2003] They suggest the biggest change is the move toward an oil free index and the
onset of gas-on-gas competition. This can really only occur in countries with competitive
gas markets. Some European countries are moving in this direction because some LNG
contracts have been linked to the price of imported Russian gas at the German border;
however, the price of Russian gas at this point is tagged to an index of oil prices. With
the advent of unregulated and active gas trading markets, the US import projects include
gas and oil indexes in their pricing structures. Should an LNG import project be
82
developed, the UK's large and active gas trading market would encourage full gas
indexing. This may not be an option for countries with a relatively infantile natural gas
industry, such as China. Although regasification plants are not yet in operation, it is
expected that the LNG pricing structure will be indexed off a 30% to 70% split between
coal and oil. [World Bank (1), 2000]
LNG buyers and sellers have no control over the price dictated by the agreed upon
indexes. This lack of control presents the supply chain participants with a great deal of
risk. In order for buyers and sellers to commit themselves to a long term contract, there
is incentive to preventing contract prices that may be too high or too low for either party
to accept. This has brought about the concept of price floors and ceilings. This creates
an "s" effect on the contract price curve similar to that displayed in Figure 51.
Contract
Ceiling
Floor
Index Price
Figure 51 - S-curve Created From Instituting a Price Ceiling and Price Floor
If both a floor and ceiling are included in the price structure, formula (1) becomes:
P, = Minimum(fJ+ 7r*K,-i , r)
(3)
where:
T
is the ceiling price
P is the floor price
83
Imposing a floor or ceiling cannot be taken for granted and is not done without
compromise.. Since the adjustment significantly reduces risk for one party while reducing
cost or revenues for another, a limitation cannot be added without a comparable contract
concession or risk reduction benefiting the other party.
Morita suggests that it is also possible for price structures to be based on fixed or
quasi-fixed prices. [Morita, 2003] This may be the case in situations where a gas
monopoly exists. There may be only one possible LNG buyer (such as a national or
state-owned gas company), and this buyer owns or has controlling interest over the LNG
venture. In this case, the value of r in formula (3) is greatly reduced, r and the
"minimum" are removed, and ,6continues as the base price.
Terms of Delivery
The terms of delivery indicate the point in the supply chain where the seller
relinquishes the cargo to the buyer. The choice of terms affects the structure of the
participants in the LNG supply chain, the pricing structure, and type and level of risk
accepted by each participant. Table 8 lists the common terms of delivery and the
respective transaction points for an LNG project.
Table 8 - LNG Terms of Delivery
Terms of Delivery
Free-on-board (FOB)
Cost, insurance, and freight (CIF) or
"landed"
Delivered ex-guay (DEQ) or "tailgate"
Transaction Point
LNG tanker manifold at the export terminal
LNG tanker manifold at the import
terminal
Gas exit point from regasification plant
The model developed for this thesis assumes "tailgate" delivery from the
regasification plant. This allows a single LNG venture to own and control all aspects of
84
an LNG supply chain. This permits a great deal of flexibility in optimizing plant outputs,
transportation, and storage. It also allows the LNG venture to fully control its
participation in spot cargo transactions. On the other hand, total control is commensurate
with total risk. In this case, the buyer assumes security of supply risk and price risk.
Security of supply risk is approachable from two directions. First, it may be seen as very
serious because the seller is committed to the buyer only through contractual obligation.
The second approach insinuates a mild amount of risk because the seller has committed
to supplying the region by owning and operating a regasification terminal. As long as the
price structure is competitive with the seller's other options, the seller has no incentive to
default. Price risk is a legitimate concern for the buyer without an ownership share in the
supply chain. Therefore, negotiating a ceiling to the price structure may require serious
concessions such as accepting a pricing structure where the average price favors the
seller, permitting a higher floor price, or agreeing to a higher minimum number of annual
deliveries.
The buyer gains contract leverage as delivery terms approach FOB. CIF terms are
common in many LNG contracts. In this case, either the buyer or a third party owns and
operates the regasification terminal. In a third party scenario, the buyer takes
responsibility of the gas at the dock but pays the terminal to provide landing, storage, and
regasification services. This process is known as tolling. [Margulis, 2003] In both cases,
the buyer reduces price risk because he can leverage a more favorable pricing structure.
However, the buyer assumes more security of supply risk because only favorable returns
and contractual obligations are keeping the LNG venture from seeking greener pastures.
The local LNG buyer must now be competitive with international buyers.
85
The playing field may be at its most even when the parties agree to FOB terms.
In this case, the buyer owns or has contracted the regasification terminal and a tanker
fleet. Security of supply risk is greatly mitigated because the liquefaction plant needs the
buyer's capabilities. If the buyer has arranged contractual relationships with the shipping
and regasification segments, the risks of owning are substituted for higher handling costs.
Of course, this is assuming that the buyer could operate shipping and regasification more
efficiently under "one roof." The buyer also leverages price agreements because
liquefaction terminals may compete for the buyer's business. Effective contractual
arrangements between supply chain segments have encouraged the trend toward joint
venture LNG projects. [Greenwald, 1998]
Returning to the discussion of price structure, terms of delivery are accounted for
within the "pass-through" coefficient 7. In a "tailgate" delivery agreement,
7r
is likely to
be highest and approaching the heating value conversion. On the other hand, w is lower
in the FOB case because the buyer assumes responsibility for transportation and
regasification. Okogu points out that this is not always the case in two examples of
import projects to Japan. The CIF contract for gas from Qatar has 7 equal to 0.1485 with
a price floor of $1.00 per mmbtu. Although the FOB contract for gas from Oman has a
higher w equal to 0.1515, the seller has not negotiated a price floor. Assuming an average
Qatar contract price of $3.50 per mmbtu delivered CIF, the net-back price according to
the Oman contract would be $2.55 per mmbtu. For a Immtpa annual volume, the buyer
would pay an equivalent of $130 million per year for transportation costs. Using the
model developed for this thesis and some conservative assumptions for repaying debt,
transportation capital and operational costs for shipping should only run in the area of
86
$60 million per year. The Qatar-based LNG venture may have landed a bargain. [Okogu,
2002]
Volume Agreements
The SPA includes a detailed schedule of monthly and annual delivery
requirements. A unique aspect to LNG SPA contracts is the "take-or-pay" clause which
requires the buyer to provide payment for the volume designated by the minimum
delivery requirements regardless of whether or not the buyer accepts delivery. This
clause ensures that the LNG venture can satisfy debt requirements and earn a meager
return. Generally, this clause holds even if the venture's gas and assets can serve another
buyer on the spot market. [Greenwald, 1998] If it were not for the enormous risk
assumed by the venture, the "take-or-pay" clause might seem completely unfair.
A buyer's ability to handle "take-or-pay" conditions is dictated by the buyer's
ability to handle other issues. A buyer may be able to accept a higher contract price if it
can keep minimum delivery requirements low, or on the other hand, a buyer may need to
keep prices low but can guarantee a high volume. However, all of these scenarios are
dependent on the nature of the buyer's business.
The buyer's customer base indirectly dictates the choice of contract it can engage
in based on the cycles of consumption and price. Using the United States as an example,
Figure 52 and Figure 53 show trends in gas consumption and gas prices across all sectors,
and Figure 54 shows natural gas wellhead prices over a similar period. Industrial gas
customers have a flat consumption profile although the prices paid are relatively lower
and reflect the trends in wellhead price. The LNG buyer serving a diversified group of
industrial customers can concede contract terms with a high minimum take while pushing
87
for a ceiling on gas prices. The buyer serving residential customers offers prices that are
consistently higher than other sectors; however, the consumption swings are massive.
This buyer can concede a price floor but needs to push for a low minimum take to last
through the summer. The commercial buyer can act along the same lines; however, the
lower consumption peaks and lower prices must justify the commitment. LNG buyers for
the power generation industry are in a quandary. Their industry pays the least for gas but
presently does not consume much. Buyers for base load natural gas power plants have
steady requirements and therefore desire consistency. If the volumes are enough, they
can concede a moderate to high minimum take but must achieve a price ceiling in return.
Due to their unpredictability and the delay involved in sourcing LNG, buyers for peakshaving plants should avoid LNG import contracts altogether.
1.2Residential
0.9-
Industfil/
0.6P
~comnmerbal
0302
JFg-AMJ JASON J
Figure 52
-
MAMJtiASONDJBMAMJ JASON
Monthly Natural Gas Consumption By Sector [ELA (11), 20021
88
--
Connercial
-I-ndustrial
El&ctric U14it
11
-390
10 .36'0
9
0
:300
2M0
7-6 -
2
4 -
-
5 -
15)
# 14,
120
-
S3
90
2
2001
2002
2003
2005
2004
Figure 53 - Average Monthly Natural Gas Prices per Consumer Sector, 2001-2003 [EIA (11), 20021
--
C4t
Gate
177
78.24D
-
6
-
4
2-
2000
2001
2002
2003
2004
Figure 54 - Average Monthly Wellhead and City Gate Natural Gas Prices, 2000-2003 [EIA (11),
2002]
89
The discussion of consumer gas requirements dictating contract terms may be
overly simplified. Large gas distributors, retailers, and merchants who are capable of
engaging in an LNG contract should maintain a diversified customer base. However, just
as there are benefits to a diversified demand base, these companies recognize the need for
a diversified gas supply. By the nature of the trade, LNG supply chains can offer
different guarantees and flexibilities when compared to traditional pipeline and storage
options. When agreeing to an LNG contract, terms and conditions can be established that
diversify price and security of supply risks inherent in an existing domestic gas portfolio.
90
Chapter 7 - Model Description
A model has been developed to gauge the feasibility of establishing LNG supply
chains. To determine the feasibility of such a project, the model returns traditional cash
flow analysis such as net present value (NPV), internal rate of return (IRR), initial capital
expenses (CAPEX) and maximum sustained after-tax net income (NI). While these
metrics gauge the overall project viability, they are dependent on minor optimizations
within the model that address specific requirements in specific areas of the supply chain.
The following section walks the reader through the key assumptions, references,
optimization structures, and general calculations that comprise the model.
Model Structure
The model's calculation structure is constructed using Microsoft Excel
spreadsheet software. A "run" is performed for single values of assorted inputs such as
LNG plant output, tax rate, and discount rate to name a few. A single run yields NPV,
IRR, CAPEX, and NI results for 414 combinations of distance and contract price.
Multiple runs were performed over small ranges of assorted inputs in order to accrue data
for project sensitivity analysis. As runs were completed, results were imported into a
simple Microsoft Access database. Although Access offers sophisticated charting and
reporting tools, complications and a lack of flexibility required the author to export
queried data parcels to another Excel program for final analysis.
91
Segment Cost Analysis - In General
The model begins with analysis of the capital and operating costs inherent to each
segment of LNG supply chains. The author has considered two means of calculating
costs for specific segments. The first method entails breaking down the cost components
of complicated projects and segments. Breaking the LNG supply chain into liquefaction,
transportation, and regasification increases the accuracy and understanding of the overall
project's costs. The same process can be implemented on the local segment level. For
example, transportation segment costs can be divided in many ways. Capital costs can be
distributed beyond simple newbuilding costs to include costs for the hull structure, the
cargo containment and handling, and the power plant. Operating costs can be traced to
fuel consumption, crewing costs, maintenance, and beyond. This method of cost analysis
is limited only by the level of detail of known costs, the variance of these costs, and the
information's availability and propriety. Driven by rapidly changing costs, technological
improvements, and the drive for competitive edge, these limitations are especially evident
in today's LNG industry. For these reasons a second method of cost analysis was
required. This method includes normalizing overall segment costs from a large range of
existing, planned, and theoretical LNG projects as well as related research.
Liquefaction Plant and Terminal
It was determined to approach the cost of liquefaction facilities from a normalized
perspective. The reasons for this are manifold. In most available information, the
influence of exploration and gas production costs is vague or omitted. However, overall
project costs for liquefaction plants, terminals and storage facilities can be available and
92
normalized on a "per ton of LNG" basis. Exploration and gas production costs are
difficult to obtain and appear to be driven by forces amongst competing companies and
host nations. Therefore, exploration and production costs are assumed to be included
within the gas commodity cost.
Figure 55 shows a range of normalized data for the capital cost of liquefaction
facilities. Data points stem from existing and planned projects by major energy
companies as well as research from agencies such as the EIA and Cedigaz and
consultants such as Drewry Shipping Consultants. Since the data is heavily weighted
toward facilities in the 2 to 4 mmtpa range, high and low averages were calculated. As
can be seen, the linear regression amongst all data points is similar to trend between
average points. Surprisingly, the data does not suggest any significant benefits from
economies of scale but rather a straight $350/tpa investment. The author attributes this to
a lack of information on the costs of newer plants utilizing larger production trains (i.e.
over 4mmtpa).
.
550
0.
All data
500+
points
450
Average
400X
points
U.'
350
__
350
-Regression,
all data
oints
p250
300200
0
4
6
2
Liquefaction Plant Size, mmtpa
8
Figure 55 - Liquefaction facility capital costs normalized per ton of annual output
93
Most sources suggest that liquefaction facility operating costs are 4% to 7% of
capital costs. [Drewry, 2001], [Greenwald, 1998] These costs include plant labor,
powering requirements, gas losses, and maintenance; however, these costs do not include
the commodity cost of the natural gas that is converted to LNG. A similar analysis of
operating costs was performed and is presented in Figure 56. Again, operating costs do
not show a significant benefit from economies of scale, but rather steady costs of about
$0.33/mmbtu per year.
3 0.70_
E 0.60
All data
-
points
a
d 0.50-
8. 0.40
Average
points
_
_U
s0. 30
0.20
--
_
_
Regression,
all data
_
00.10a: 0.00
0
2
4
6
8
Liquefaction Plant Size, mmtpa
Figure 56 - Liquefaction facility annual operating costs normalized per ton of annual output
Transportation
Transportation costs are limited to the capital and operating costs for the required
fleet of LNG tankers. Terminal facilities are included in the costs of liquefaction and
regasification facilities. In order to determine total capital and operating costs, a fleet
optimization was performed. Based on specific trade route distance and an annual level
of liquefaction plant output, the optimization seeks the most economical match of fleet
94
size, tanker size, and vessel speed. The optimization metric is net present value. These
calculations could be further complicated by the decisions for cargo containment systems
and the type of propulsion plant. Since the purpose of this model is to examine the entire
project and not the idiosyncrasies of any single segment, assumptions were made based
on the average of best and worst cases.
Newbuilding Costs
An assumption for newbuilding costs was based on the current trends in the
shipbuilding market. Figure 57 shows how newbuilding prices have declined since the
early 1990's, and the polynomial trendline gives a solid indication of market prices over
this period. The crest during the early 1990's is a carry-forward of conservative pricing
based on a small number of orders. As orders increased, construction technology
implementation and competition drove down market prices. As is evidenced by the
plateau in current newbuild contract prices, recent technological gains have been
completely realized. Unlike most other shipping segments, the influence of a second
hand market and occurrences of scrapping only affect newbuilding prices by their virtual
absence. Since LNG tankers are nearly always constructed as part of a long term, costly
supply chain, there is currently a noticeable absence of readily available tanker tonnage
and unclaimed LNG available for shipping. Since high-quality LNG tankers realize a life
span much longer than the traditional 25 years and the LNG shipping industry is
relatively young, LNG tanker scrapping is currently negligible. Based on available
newbuilding data assembled by Maritime Business Strategies, LLC, this model assumes
$165 million to be the cost of a new 137,500 cubic meter LNG tanker. [MBS], [Drewry,
2001]
95
$300
$2,500
$$/cubic meter
*
137,500 cm
Poly. (137,500 cm)
!
-
""""oly.($/cubic
meter) ,
250
$2,000
$200
$1,500
$150
$1,000
~$100C
41
$50
$500
Note: ForallLNG
tankers over 90,000
cubic meters.
$0
1993
1995
1997
1999
Year
2001
2003
2005
$0
2007
of Delivery
Figure 57 - Trends in newbuilding prices normalized for capacity (left axis) and calculated for a
137,500 cubic meter tanker [MBS]
Tanker Size and Speed
In order to limit the size of an LNG fleet, it seems logical to optimize tanker size
and speed. However, there are several obstacles toward an accurate optimization. To
begin, LNG tanker capacities for traditional large scale projects have ranged from 90,000
to 140,000 cubic meters with newbuildings contracted up to 145,000 cubic meters. The
majority of tankers built during the current construction boom are above 125,000 cubic
meters. Similar to oil tankers, a small increase in the capacity of an LNG tanker may
necessitate the acquisition of fewer tankers to meet LNG delivery requirements.
Unfortunately, the recent drop in LNG tanker newbuilding prices prevents the
development of a matrix of vessel cost and capacity. Therefore, this model provides a
linear price correction discount or premium for optimized capacities above the base case
of 137,500 cubic meters. The price correction is calculated by multiplying the difference
96
between the optimized and base case capacities by the normalized newbuilding cost for
the base case capacity. For instance, a 140,000 cubic meter vessel is 2,500 cubic meters
larger than the base design. At $165 million for a 137,500 cubic meter tanker, the
normalized newbuilding cost is $1200 per cubic meter. Therefore, the premium for a
140,000 cubic meter tanker is calculated to be $3 million.
Since a suitable newbuilding matrix containing cost and vessel speed could not be
developed either, similar logic is employed in determining a proper correction for
optimized cruising speed. The cube law that relates horsepower and speed was used to
determine the required horsepower for the optimized tanker. The cube law is as follows:
3
HorsepowerBseCase
Speedptinized,
Horsepoweroptimized
The base case horsepower is 40,000bhp. In order to determine the premium or discount
SpeedBaseCase
for the optimized power plant, an assumption for the cost of the base case power plant
was required and was estimated at 15% of the newbuilding price. The linear correction
was then calculated as follows:
(Horsepoweropimi.es
Correction= Newbuilding _ priceBase x 15% x j
-1
HorsepowerBaseCase
Table 9 shows the capacities and speeds eligible for selection within the
optimization model. The limited range of capacities was chosen in order to include only
a small, realistic stretch from current shipbuilding capabilities. In addition, the author
feels that much larger and/or faster LNG tankers will have hull forms and subsequent
power requirements that are not as simply scalable from the base case tanker. Tankers
approaching 200,000 cubic meters would likely depart from traditional steam plants and
boil-off procedures that would bring unnecessary complications to the overall model. By
using a small range of capacities and speeds in the model (as opposed to a fixed design),
97
The model is less likely to yield results that are served by a small fraction of a tanker.
For instance, if 8.1 tankers are required for a trade route, the model assumes 9 tankers are
built, but the results suffer accordingly. A small range of options prevents this from
occurring.
Table 9- Candidate tanker capacities and cruising speeds
Capacity
Cruising Speed
Cubic meters
knots
130,000
16.0
137,500
16.5
140,000
145,000
17.0
17.5
150,000
18.0
18.5
19.0
19.5
20.0
20.5
21.0
21.5
22.0
Fuel Consumption
Based on data for comparable existing ships, the base case tanker bums 180 tons
of bunker fuel per day for propulsion and power generation. This value is normalized to
plant horsepower in order for scaling to the optimized plant size. An additional 12 tons
per day is required to power compressors and heaters necessary for burning boil-off.
Bunker prices are estimated at $125 per ton.
Cargo Boil-off
Various sources have estimated daily LNG boil-off at 0.10% to 0.15% per day
based on the type of containment system and the degree of insulation. This model
assumes 0.125% of LNG cargo per day will boil-off and be used as fuel in the ship's
98
steam plant. Boil-off is charged to the carrier at the delivered contract price of the gas
(i.e. price after regasification) and is included as an operating cost.
Heel
Suggested values for heel range from 1.5% of capacity to 5,000cm. For the base
case, a heel of 2,250cm was selected. This is equivalent to 1.6% of capacity. [Drewry,
2001], [Greenwald, 1998]
Miscellaneous Vessel Operating Costs
Costs necessary for the daily operation of tankers include costs for crewing,
maintenance, provisions and stores, office overhead, and insurance. Based on investor
information from LNG operators Leif H6egh and Golar LNG, a value of $10,000 per day
per tanker is considered appropriate. This daily rate remains constant for all optimized
tankers. [H6egh, 2002], [GolarLNG]
Dry Docking Costs
Since vessel drydockings are considered substantial improvements to the assets
value, the related expenses are capitalized over the period of time between dockings.
Leif Hdegh estimates the amortized cost at $2,300 per day. [H6egh, 2002]
Taxes and Depreciation
The base model assumes that the shipowner or joint venture is subject to a 40%
tax rate. Using the straight-line depreciation method, the tanker is fully amortized over a
useful life of 30 years at which time the tanker will be worthless. The transportation
optimization utilizes a 15% discount rate. Lastly, the optimized transportation plan is
99
analyzed over a 12-year span because traditional ship financing periods range from 10 to
15 years.
Regasification Plant
Since detailed information for the capital cost of regasification facilities was not
available, a normalized approach to gauging capital costs was implemented. Similar to
the method used for liquefaction plants, overall project costs for modem regasification
facilities were determined from existing and proposed facilities as well as research
estimates. Regasification capacity is determined from the amount of gas delivered from
the ships. Due to heel and boil-off, the regasification terminal receives less than the LNG
plant exports. Figure 58 illustrates the strong economies of scale that exist for
regasification facilities. These facilities are typically the least costly segment of the LNG
supply chain. Due to a lack of information on the subject, operating costs are not
included in the model. However, regasification losses are included. Natural gas is
consumed in the regasification process at an estimated rate of 2.5% of gas delivered to
the terminal. [Larson, 2003] Furthermore, this model does not consider offshore
regasification terminals as an option.
100
1000
.0
i
0
900
800
700
600
500
400
o
'6"'
300
200
100
o
0
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
Regasification Capacity, bcf/d
Figure 58 - Trend in normalized capital costs for regasification facilities
Cash Flow Analysis
Engineering Costs
The model does not take all pre-construction costs into account. Costs required
for concept and preliminary supply chain designs and front end engineering and designs
(FEED) are not included; however, in most cases, the data used to develop liquefaction
plant costs include engineering costs incurred by the engineering, procurement, and
construction (EPC) contractors.
Investment Schedule - Liquefaction and Regasification Plants
The schedule of payment for the construction of liquefaction and regasification
plants varies with respect to the unique terms of the EPC contract and the financial
strength of the players involved. [Greenwald, 1998] Since the liquefaction plant is the
most capital intensive asset, the model spreads the cost equally over each year of
construction prior to production and an initial payment at contract signing (i.e. year 0).
101
The model's base case dictates that construction of both plants is completed at the end of
year 3. The regasification plant payments are spread equally over years 2 and 3.
Investment Schedule - Tankers
LNG tanker payment schedules follow the precedents set in the world
shipbuilding industry. [Greenwald, 1998], [Drewry, 2001] Although there is often a
payment at contract signing, the model issues the first payment at the time of keel-laying.
The payment schedule is shown below in Table 10. The model uses 30 months as the
time required to build a LNG tanker.
Table 10 - LNG tanker model payment schedule
Payment
Number
1
2
3
4
Construction
Schedule
0% keel-laying
1/3 complete
2/3 complete
Delivery
Payment Amount
10% of vessel price
25%
25%
40%
"Build up"
The model assumes that the LNG plant starts at and steps up daily capacity
annually by 333,333,333 cubic feet of natural gas. [Greenwald, 1998] This is equivalent
to 5.3 million cubic meters of LNG per year or 2.27mmtpa. The model assumes that
ample ships can be supplied each year to meet the entire annual shipping requirement.
Plant production begins in year 4.
Taxes and Depreciation
The need for depreciation in a multi-national business environment is certainly
debatable. There is no doubt that companies who introduce and operate LNG
import/export projects will negotiate stellar tax incentives with host nations. That is, of
102
course, if they cannot devise a means to avoid taxes altogether. To be conservative, the
model assumes an exogenous tax rate of 40% for the base case.
Since the period of evaluation for the model's projects is 20 years, the
liquefaction and regasification plants are fully depreciated over the evaluation period. It
is unlikely that a liquefaction plant would be built to draw from gas fields with less than
20 years of producible life, but there is great uncertainty in estimating a large field's
expected lifetime. Since regasification plants are typically built to service a specific
liquefaction plant, the regasification plant is viable as long as the liquefaction plant
continues to produce. The ships, on the other hand, are depreciated over a useful life of
30 years. Such a tanker could last over 30 years, but it would probably require life
extending modifications/betterments. Although unique to the LNG trade and dependent
on LNG plants, these tankers are flexible to just about any LNG trade route.
Ownership and Capital
Options for funding the project range from bond issuance, bank loans,
state/national loans, venture capital, and through equity. The options are further
complicated by the choice of ownership structure. A separate analysis in itself would be
necessary to gauge the effectiveness of each ownership and financing combination with
the accuracy comparable to this model. For these reasons the model assumes the project
is financed with 100% equity capital through a joint venture.
Contract price
Not to be confused with "commodity price," the contract price represents the
selling price of the imported gas at the "tailgate" of the regasification terminal. In the
cash flow analysis, the contract price is required to calculate the income generated from
103
gas sales to the customer. Typically, this price will correlate with the floating pricing
structures, terms and conditions discussed in Chapter 6. Since pricing structure specifics
for LNG contracts are confidential, the choice of contract price is up to debate. Average
LNG import prices are available from the EIA and IEA; however, the data does not
accurately represent "tailgate" prices. To avoid confusion, the base contract price was
determined to be LNG's competing gas price (i.e. pipeline gas prices). In the United
States, the most accurate competing gas price was a state's average city-gate price. In
Europe, the price of imported pipeline gas was chosen as the contract price. In South
Korea and Japan, there is adequate and reliable data for the average price of imported
LNG. Gas pricing information of any kind for the remaining Asian nations was
unavailable. Although a correlation to oil prices is most likely, the average South Korean
LNG price is used in these cases.
104
Table 11 lists the choice of contract prices at selected import locations.
105
Table 11 - Contract price selections for the model's base case. All prices in $/mmBtu [IEA (2), 20021,
[EIA]
Average
Import Terminal/
MaktLNG
Market
Price
Boston, MA
Baltimore, MD
Savannah, GA
Lake Charles, LA
Import
Price of
Pieie
Pipeline
Gas
2.92
3.20
Tijuana, Mexico
Los Angelos, CA
San Francisco, CA
Seattle, WA
UK
Zeebruge, Belgium
Montoir, France
Portugal
Fos, France
Barcelona, Spain
2.79
2.34
2.93
2.34
3.03
La Spezia, Italy
Piraeus, Greece
Tokyo, Japan
South Korea
Shanghai, China
Taiwan
Hong Kong
Thailand
Mumbai, India
2.93
2.93
3.94
4.00
4.00
4.00
4.00
4.00
4.00
3.57
2.84
2.16
2.75
2.16
2.91
2.75
2.75
CiyMoe
gate
Model
Contract
Price
Price
4.73
4.75
4.22
3.65
3.79
3.79
3.79
3.41
4.73
4.75
4.22
3.65
3.79
3.79
3.79
3.41
3.57
2.84
2.16
2.75
2.16
2.91
2.75
2.75
3.94
4.00
4.00
4.00
4.00
4.00
4.00
Commodity Price
The commodity price is the cost of an mmbtu at the entrance to the liquefaction
plant. The commodity price reflects the cost of exploration, drilling, producing, and
transporting the gas to the plant. This price also reflects royalties paid to the host nation
for using their natural gas. In other larger and more detailed models containing analysis
of pre-LNG plant costs, the commodity price may reflect only the raw material royalties.
This value can vary significantly from project to project and is subject to negotiation
(especially with respect to royalties). The model uses $0.70 per mmbtu as the base case
value.
106
Discount Rate
Choice of discount rate strongly influences the net present value results. A
company's choice of discount rate mainly reflects the cost of financing, expected return
(somewhat based on the performance of existing comparable projects), the perceived
level of project risk, and inflation. [de Neufville, 1990] Although the model eliminates
financing through the 100% equity assumption, a more meaningful expected return may
be required. Before the recent economic slump, this would encourage discount rates in
the area of 12% to 15% (Greenwald). A discount rate of 10% is utilized in the base case
due to the recent economic slump (and therefore the lack of other good natural gas
options) and due to the delay incurred before profitability which hammers NPV results if
a high discount rate is chosen. In the best situations, this delay is about 4 years. Inflation
is not considered here because inflation is likely to "net-out" due to inflation clauses in
the sale and purchase agreement.
Distance
Distance and contract price are the primary inputs that are adjusted in the model.
In fact, the model could be operated to yield a vast matrix of results for individual and
abstract arrays of contract price and distance. In order to give the user a more tangible
feeling of the viability of model results, the abstract array of contract prices has been
removed in favor of the market-specific base case value discussed above. Furthermore,
the idea for a single abstract array of distances has been substituted by a two dimensional
matrix of distances linking potential import and export locations. Import locations
selected for the model were based on existing and formally proposed locations as well as
locations rumored to be under consideration. Information was collected mainly through
107
newspaper articles and various energy, maritime, and oil and gas industry magazine
articles. Export locations were chosen similarly but are limited to areas serviceable by
major gas fields. The distance matrix is presented in the appendix, and Table 12 lists the
import and export locations included in the model.
Table 12 - Import and export locations selected for inclusion in the model
Import Locations
Export Locations
Boston, MA
Baltimore, MD
Savannah, GA
Lake Charles, LA
Tijuana, Mexico
Los Angelos, CA
San Francisco, CA
Seattle, WA
UK
Zeebruge, Belgium
Trinidad
Arzew, Algeria
Lagos, Nigeria
Stavanger, Norway
Bushire, Iran
Muscat, Oman
Abu Dhabi, UAE
Ras Laffan, Qatar
Bontang, Indonesia
Brunei
Montoir, France
Portugal
Fos, France
Barcelona, Spain
La Spezia, Italy
Piraeus, Greece
Tokyo, Japan
South Korea
Shanghai, China
Taiwan
Hong Kong
Thailand
Mumbai, India
Dampier, Australia
Kenai, Alaska
Maracaibo, Venezuela
Recife, Brazil
Callau, Peru
St Petersburg, Russia
Sakhalin, Russia
Novorossisk, Russia
108
Chapter 8 - Results
The model developed for this thesis is intended to serve as a tool for evaluating
the conceptual feasibility of LNG supply chain development. Feasibility requirements of
such a project change from user to user. Table 13 lists the users who would find such a
model beneficial.
Table 13 - User Specific Purposes for Using a Feasibility Model
User
LNG venture
Purpose
*
Profitability and cash flow analysis
*
Comparison of import/export options
Initial estimate of shipping requirements
Export nation and/or
national energy
company
Import buyer
Project contractors
Import facilitators
E
Determine viable trade routes that can pay the most for their gas
n
Define target commodity price on all possible routes
I
Evaluate potential suppliers' costs
*
Assist in rating the risk of supply
*
Gauge the viability of a niche construction market
*
Research the effect of a bid on the project's bottomline
*
Refine bid.
I
Find and estimate a piece of a project's profit
*
Evaluate the project's need for subsidies
*
Estimate capital requirement
Assist in gauging risk
Investors
Results discussed below demonstrate the abilities of the model and the manners in which
specific users would interpret the results. The results are also presented in a manner such
that all possible import or export locations are evaluated.
109
Project Viability
Net Present Value (NPV)
After tax project cash flows are discounted to time 0, in order to gauge the
profitability of a project. A negative NPV indicates the project does not meet the return
expectations set forth by the selected discount rate and evaluation period. According to
this metric, the best choice is the project with the maximum NPV. [de Neufville, 1990]
Figure 59 illustrates the fifteen best supply chain alternatives under the base case
conditions. Baltimore and Boston dominate the scenarios because they have the highest
contract prices. These results are of most interest to potential LNG ventures, investors,
and project contractors. The LNG venture is interested in establishing the most profitable
LNG project possible. Similar to some members of the venture, outside investors who
are interested in diversifying their portfolios with LNG projects are interested in what
appears to be the least risky project. The conclusions delve on this further. Plant
engineering and construction companies that can handle large international projects and
shipping companies want to focus on the most profitable supply chains because they may
be able to impose better margins on their bids. This is especially true in the largest LNG
projects where only a select few companies have the resources to handle the entire
project. [Greenwald, 1998]
110
Best Supply Routes
Under Base Case Conditions
$1,400
$1,200$1,000-
.2
S$600
$400
-
$200 E
N
5
'N <
5 g
0,
-
0
(LI
0-0
FiS
oic
th0riia
e5
-LGsppycaor
Veeul
02
CO
Cl)
to
0wihte
-M
Batmresp
*E
0
NE
G
~~I
C
CDj
-
amp
>Naid
0
CD020
- =
NPV
otfaoal
U)5
appar
0'aQ
.
T seeE
han
to
be
mos
facilitators. Before the gas buyer commits to an SPA, the buyer needs to get a feel for the
supplier's margin. As discussed earlier in the chapter on contracts, the buyer must be
confidant that he is not accepting too much risk when he agrees to a pricing structure On
the other hand, the buyer's security of supply is in jeopardy if the LNG venture cannot
cover costs. [Greenwald, 1998]
111
Plant Outputl6 Commodity Cost $0.70 Tax Rate 0.4 BasePriceFraction I Discount Rate
0.1
Equity Portion 1 Period 20
Net Present Value, Base Case
$1,500
_
NPV
$1,000-
M
$500
Import Terminal
M Baltimore, MD
$0-
.2
-$1,000
R.T
1
2
cc
F
i
~
-r- m-
CP
D >p z
N
-
<i
1920
e
(V
Dis
Dac
ED
0
t
p
TerminAM
i
-J
MD
U
0
cs
b
c
:3
m
3200 3750 3773 3940 4800 5064 5666 6900 7991 8245 8348 8577 10000
10700
11530
DistancelExportTria
Figure 60 - NPV of import options to Baltimore, MD under base case conditions
Import facilitators such as governments and port authorities have an interest in the
LNG project's profitability. Before governments and port authorities consider offering
concessions (i.e. land or tax benefits) to the LNG venture in order to attract the terminal
to a specific port, country, or state, these parties should have an indication of the
venture's ability to last. If the profitability is especially good, these parties will be
looking to carve out a piece of the profits in the form of special imposed taxes, higher
port charges, or forced allowances for security.
Internal Rate of Return (IRR)
The internal rate of return (IRR) is the percentage rate that causes the discounted
present value of the benefits in a cash flow to be equal to the discounted present value of
the costs. [Steiner, 1992] This is equivalent to the discount rate that causes the net
112
present value of a cash flow to be zero. Interpreted comparably to the NPV presentation
in Figure 59, Figure 61 illustrates the supply routes with the best IRR. In this case, the
main difference between the NPV and IRR metric is that IRR does not give a good
indication of the scale of the project. For instance, despite a 15% lower contract price,
the Oman to India trade is among the 15 best routes in Figure 61 because transportation
costs are much lower. It appears to be a worthy option, but Baltimore is still the best
choice for an import location.
Plant Output 6 Equity Portion 1 Commodity Cost $0.70 Tax Ratel0.4 BasePriceFraction 1 Discount Rate 0.1
Maximum Internal Rate of Return for All Supply Routes
Under Base Case Conditions
16.0%
Max IRR
________
15.5%
-
-
c 15.0%
-
14.5%
x14.0%
13.5%
13.0%
J
12.5%6J
.2
d~ .2
.0 dE .o0.
90
(
~
c 0< -? 22
0
20
>0
0
S?<
M<
E (60 ) c0 E
<0 2E02
o
90
>2.
(L6Zc
WM
-C
1:
.
.0
4)u,
CC0
0
0
=j.
5
Route
Figure 61
-
LNG supply routes with the highest internal rate of return under base case conditions
Figure 62 reconfirms the findings in Figure 60 because when comparing export options to
only one import terminal, distance is the primary factor affecting the variance of project
return. It is important to realize that the same users of NPV metrics utilize IRR metrics to
fortify their NPV findings.
113
IPlant Output 6 Commodity Costl$0.70 Tax Rate 0.4 BasePriceFracton 1 Discount Rate 0.1 Equity Portion 1 Interest Rate
0 Period 20
Internal Rate of Return
Under Base Case Conditions
IRR
16/%
14%
12%
e 10%
o
Import Terminal
MD
%Baltimore,
6%
4%
-
2%
0%
owM
2
V
M
if
W
-F
M
U)
go
>
1920
z
C
ci
0~
ccCO
C
wef C5
.2
5, S
w
0'C
0 >J
3200 3750 3773 3940 4800 5064 5666 6900
7991
8245 8348 8577 10000
10700
11530
1Distance Export Terminal
Figure 62 - Internal rate of return for export locations to import into Baltimore under base case
conditions
Capital Expense (CapEx)
The CapEx metric is no more than the sum of all capital investments required for
a project. For the LNG venture participant(s), CapEx is an indicator of the exposure to
risk within the project based on the investment they stand to lose. This is especially true
for an equity based venture. For a venture or company seeking outside financing, CapEx
metrics, when compared to balance sheet debt and equity information, influence the
extent of financing available. This metric indirectly aids in determining who and how
many participants will be needed in the LNG venture.
A large drawback to CapEx as a stand alone metric is that it is not a good
indicator of project viability. In fact, the base case's best supply route option of Algeria
to Barcelona at $2.75 billion is not among the 100 best IRR routes. By examining Figure
114
63, the best option of importing LNG into Baltimore from Trinidad or Venezuela costs
$3.1 billion up front under base case conditions. Of all base case routes considered, these
two routes have only the
3 8 th
Plant Output 6 Commodity Cost $0.70 Tax Rate
lowest capital expense.
0.4
BasePriceFraction 1 Discount Rate 0.1 Equity Portion 1 Interest Rate
0 Period 20
Capital Expense, Base Case
CC00CapEx
_____________
$6,000
-
$5,000
$4,000.
$3,000-7
import Terminal
MBaltimore, MD
-
$2,000
-
$1,000
N
$0
1920
.
1
3200 3750 3773 3940 4800 5064 5666
6900 7991 8245 8348 8577 10000
10700
11530
Distance Export Terminal|
Figure 63 - Capital expense required for trade routes importing to Baltimore
conditions
under base case
CapEx metrics from this model are also useful to the various contractors involved
in the potential project. This is especially true for contractors bidding on the liquefaction
plant engineering and construction. By analyzing the LNG venture's options, the bidding
contractor can estimate the maximum bid that the venture can afford. Bidders will
present a negotiable tender and with adequate built in "wiggle room." Although capital
shipping costs may rival capital liquefaction plant costs, there are enough shipyards
producing relatively standard ships that there exists a market price for LNG tankers.
Therefore, individual shipyards are not likely to find this metric beneficial.
115
Net Income
In this case, net income refers to net earnings after taxes and interest. This model
does not consider interest, but the model does account for depreciation in the tax
calculation. Net income from each year in the cash flow analysis is used to calculate
NPV and IRR and therefore is already somewhat included in prior metrics. With regards
to gauging project feasibility, net income is most useful when analyzing project
sensitivities which are discussed in detail in the next section. For lenders, investors, and
shareholders, this is a metric required to simulate the project's effects on the venture's
financial statements.
Sensitivity Analysis
The sensitivity analysis measures the change of a metric based on an incremental
change in a constraint. There are three principal reasons for utilizing sensitivity analysis
with this model. They can be categorized as follows: project design optimization,
contract optimization, and risk analysis.
Project Design Optimization
The model's base case assumes LNG plant output to be 6mmtpa. However, the
LNG venture may be interested in a larger plant because another million tons of LNG
delivered to Baltimore increases revenues by almost $250 million per year. From Figure
64, the added revenues translate into less than a $200 million addition to NPV. In fact,
Figure 65 shows that the change in IRR is slightly negative. In this example, only
projects involving Persian Gulf and Arabian sources do not exhibit diminishing returns.
However, the sensitivity of doubling the amount of imported LNG into Baltimore has a
116
diminishing effect for all sources as per Figure 66. The logic behind the diminishing IRR
is twofold. All of the capital costs for the larger plants are paid over the first 4 years, but
the full benefits do not kick in until after production build-up in year 9.
Import Terminal Baltimore, MD Commodity Cost $0.70 Tax Rate 0.4 BasePriceFraction 1 Discount Rate
0.1
NPV Sensitivity to I mmtpa (16.6%) Output Increase from Base Case
$250
$200
$150_
$100_
Plant Output
-$50
0
_$10C
1920
3200
3750 3773 3940
4800
5064 5666 6900
7991
8245 8348
8577 10000
10700
11530
Distance Export Terminal
Figure 64 - NPV sensitivity of increasing LNG imports into Baltimore by lmmtpa under base case
conditions
117
Import Terminal Baltimore, MD Commodity Cost $0.70 Tax Rate 0.4 BasePNceFraction 1 Discount Rate
0.1
Internal Rate of Return Sensitivity to 1mmtpa (16.6%) Output Increase from Base Case
0.200%
*
0.150%
+
0 0.100%
0.050%
Plant Output]
M7
0.000%
E -0.050%
-0.100%
-0.150%
-0.200%
32
m
T B
0
40
L77
3
56
1s a
,Cyr
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o
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Dsac Exw r TerminV5
=
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-
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c
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190
8 2 4M
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-160
Pistanc uprtputial
0-1.000%
1920
01000%
3200
5666 6900 7991
3750 3773 3940 4800 5064
8245 8348
8577 10000
10700
11530
Distance Export Terminal
-1.200% --S,-0.200%
Figure
6 -
~
into Baltimoreta under base case
IRR sensitivity oa1 increasi importdN
~-+
conditions
0
<0C
Fiur
1200 - I estvt t
motdLN
0%icesei
~
2
noBlioe ne
aecs
WcondCtCons
118
Choice of plant size is further complicated by the choice of discount rate. Since
the construction and build-up period takes at least over 4 years, a lofty discount rate can
kill a LNG project's chances. In the UK import example in Figure 67, a discount rate
above 11% on the best import option (from Norway) yields a negative NPV. Figure 68
shows the NPV sensitivity under base case conditions.
Import Terminal UK Plant Output 6 Commodity Cost|$0.70 Tax Rate
0.4
BasePriceFraction 1 Equity Portion 1
Min and Max Ranges of NPV For a Range of Discount Rates, Base Case Conditions
NPV_____
$2,500
-
$2,000
Best Case
$1,500
$1,000
e
FEASIBLE
-$500
S$0
-+o-Sakhalin, Russia
-e-Stavanger, Norway
6
$500-$1,000
UNPROFITABLE
-$1,500
Worst Case
-$2,00
0.05
0.06
0.07
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0.16
DsunRate
Figure 67 - Minimum and maximum NPV's a range of discount rates for import routes into the UK
under base case conditions
119
Import Terminal UK Plant Output 6 Equity Portion 1 Commodity Cost
0.7
BasePriceFraction 1 Tax Rate 0.4
NPV Sensitivity to a I Point Decrease in Discount Rate from the Base Case
Delta NPV
-
$250
$150
0
-
Discount Rate
*0.1
00.09
- $100
E
$50
$0
Distance Export Terminal
Figure 68 - NPV sensitivity to a
1 point discount rate reduction on UK import routes under base case
conditions
Contract Optimization
As has been discussed, the terms and conditions of the SPA are critical. When the
parties agree to a pricing structure, an average price can be forecasted, and most certainly
a working knowledge of what the average price could be influences the agreed upon
pricing structure. Figure 69 indicates that a 10% increase in average contract price has a
great effect on net income that ranges from 24% in Montoir, France to 13%/ in Boston.
The French options benefit more from price increases because they do not profit enough
to pay taxes, but they also lose relatively more to a price decrease because their net
income is low relative to other import markets. In general, venture participants, their
shareholders, and creditors are interested in the effect these potential gains and losses
have on financial statements.
120
Figure 70 reinforces the idea that project viability hinges on pricing structure. A
10% change in contract price affects the IRR by over 1.5 percentage points which may be
a critical difference on a marginal project. The figures depict the change in contract price
through the field "Base Price Fraction" where a value of 1 represents the base case
average contract price and 0.9 or 1.1 represents 90% or 110% of the base case average
contract price.
JPlant Output 6 Commodity Cost
0.7|Tax
Rate 0.4 Discount Rate
0.1
Average Net Income Sensitivity to a 10% Contract Price Increase at all Import Markets
Average Net Income Delta
E
20%
C
z 15%
BasePriceFraction
E1.1
0
C
10%
S5%
UN
cv~E
=.
<L
0
M
2
.2
W<~
c.
<
i
-
(
0%
U)
LLcv Cc
'U
0
Ci
0
>
COa,
import Terminal
Figure 69 - Average net income sensitivity to a 10% increase in contract price to all import markets
under base case conditions
121
Plant Output 6 Commodity Cost
0.7 Tax Rate 0.4 Discount
Rate
0.1
Average Internal Rate of Return Sensitivity to a 10% Contract Price Increase at all Import
Markets
Average IRR Gain_
-
1.50%
C
0
-2.00%
SBasePriceFraction
1.50%
0.00%
8
E
00~
m
W
NL
C
Termi
0 -XD
3
CL
cm
D
Figure 70 - Average IRR sensitivity to a 10% increase in contract price to all import markets
under
base case conditions
Risk Analysis
Due to the long term nature of the returns from this project, the model captures
certain factors that can increase the overall risk of the project. Market risk is exhibited in
the fluctuation of gas prices. Although a fair pricing structure may have been agreed to
between gas buyers and the LNG venture, it is still possible for the project to operate at
reduced revenues. Sensitivity to contract price variation was explored earlier in this
chapter in the section on contract optimization.
Political risk can occur on numerous fronts. The model is capable of capturing
sensitivities to tax rate and commodity price. Depending on how and where the
ownership is established, the LNG venture may or may not be subject to income taxes. It
is likely that the income tax rate will change over the course of the project's life. Figure
122
71 shows the net income sensitivity to an increased tax rate from 40% to 44% for an
import project into Baltimore. These sensitivities translate into as much as a 0.73
percentage point reduction in the project's IRR as per Figure 72.
Import Terminal Baltimore, MDI Plant Outputl6l Equity Portion 11 Commodity CostIO.7 IBasePriceFractionl I Discount RatelO.11
Net Income Sensitivity to a 10% Tax Rate Increase from the Base Case
Delta Net Income
$-$
-$10
'
-$15
=-$20044
E
-$25
-
-$30--$35
10
2
0
W
cc
3
7
0
0lLO
z
1920
3200
3750
3773
3940
4
4
'a
a
E
E
D
7991
8245
o
-
-$40-
0
2
4800
5064
5666
6900
8348
8577 10000
10700
11530
Distance Export Terminal
Figure 71 - Net income sensitivity to a 10% increase in the tax rate for LNG imports into Baltimore
under base case conditions
123
Iimport Terminall Baltimore, MDJ Plant Outputl6l Equity Portion 1 Commodity CostlO.7BasePrceFraction 111 Discount Rate
0.1
Internal Rate of Return Sensitivityto a 10% Tax Rate Increase from the Base Case
Delta
IRR
-
0.00%
-0.10%
U -0.20%
CD
-0.30%
Tax Rate
00.44
1004
* -0.40%
I-
-0.50%
-0.60%
lax -0.70%
-
-0.80%
Distance Export Terminal
Figure 72
-
IRR sensitivity to a 10%
increase in the tax rate for LNG imports into Baltimore under
base case conditions
Political risk may affect commodity price if the nation supplying natural gas to
the LNG venture chooses to raise prices. The reasons for such an occurrence could range
from increased inflation, regime change, and/or increased national debt. The sensitivities
are similar to those found with contract price. Figure 73 and Figure 74 show that a 10%
increase in commodity price from $0.70 to $0.77 per mmbtu results in an average annual
loss of $13 million per year in net income while IRR drops 0.3 to 0.5 percentage points
for nearly all import projects. Since the French projects do not profit enough to pay
taxes, their IRR drops almost a full point.
124
Plant Output 6 Equity Portion 1 Interest Rate
0 Tax Rate 0.4 BasePriceFraction
1 Discount Rate
0.1
Period 20
Average Net Income Sensitivity to a 10% Commodity Price Increase at all Import Markets
Average Delta Net
$0 .~
Income
-$2
-$4
..$6L
0
Commodity Cost
00.77
007
-$8
C-$10
E -$12
-$14
-$16
$18
.
0
as cnditc<ns
k<e <
0
a
ra g =
.
-
-RR
.3%
0
u 0.
.7
2
CD
.2uLL
(D
Import Terminal
Figure 73 - Average net income sensitivity to a 10 increase in commodity price on imports to all
2
6.
.
) markets
;LZ
1
under base case conditions
Plant Outputl6lEquity Portionj 1 lInterest RateJ0 Tax Rate 10.41 Base~rceFraction 11IDiscount Rate 0.1 jPeriod 201
Average Internal Rate of Return Sensitivity to a 10% Commodity Price Increase at all Import
Markets
alAverage Delta IRR
-0.60%
0.00%
0
1.00%-
LLU
-
-0.10%/
*~-0.20%
-0.30%
o
Comnmodity Cost
4%2~~
00.77~
*-0.50% -
~~~*.
-0.60%
-0.70%
00%
S E
LL LL COc a 0
5
r- CCCu
Cu
C0<C
TC
2 Cu
O
u
C
2 22
0
uCu
0
~C
j0
N
5
>CI-c
W
C
r
M
c
jImport Teffnlnal
Figure 74 - Average IRR sensitivity to a 10% increase in commodity price on imports to all markets
under base case conditions
125
Commodity Price Optimization
It has been demonstrated by interpreting the model's results in the manners above
that valuable insight can be gained into the feasibility of establishing an LNG supply
chain. Since nations with significant gas reserves may be interested in selling their gas
internationally by means of liquefaction, this model is of particular use to them as well.
When examining the LNG venture's options with the model results, it becomes evident
that particular exporters enjoy advantages due to their proximity to potential importers. It
is possible for gas producing nations to defeat this distance disadvantage by offering their
gas for a lower commodity price. The model has been geared to perform this calculation
and the results are shown in Figure 75 with regards to importing LNG to Baltimore. For
instance, by offering their gas for 30% less than Trinidad or Venezuela, Peru is able to
offer a supply chain with the same NPV. On the other hand, if an LNG venture finds the
political instability in the Venezuela/Trinidad area undesirable, the venture would know
through the model that they need to negotiate Peru down 30% and Brazil or Algeria down
over 40%.
126
IPlant Output 61 Equity Portion 1 Interest Rate 10Commodity Cost (All) Tax RateO.41BasePriceFraction 11 Discount Rate 0.1 Period 20
Commodity Price Reduction Required to Compete with Best NPV
Commodity Price Redux
100%
Comptiiv
90%joten
0.80%
070%--
o)
-
60%
50%
Import Terminal
DBaltimore, MD
Caomin0
Concess ons in exces~s
40% -f-fmntn
-h
commodity price
are
-
30%
-
S20%
10% ----
:
0%
~
EV
6
wG
CO
M.
>
1920
~>
4
CO
-
z
0
)
o)
0n
0
.
aJ
E
gcCO
0
M
3200 3750 3773 3940 4800 5064 5666 6900 7991 8245 8348 8577 10000
10700
11530
IDistanceIExport Terminall
Figure 75 - commodity price reduction required by export nations to compete with the best NPV
exporters for import into Baltimore
It is also possible to turn this data around in order that the export nation can
determine its best options. Figure 76 presents the commodity price that Trinidad must
offer to match the best NPV in all other import scenarios. In this case, Trinidad is
fortunate and should try to encourage an LNG project that imports to the US East coast
markets. The opposite is true in Figure 77 where Dampier, Australia is not the best
match on any import route. In this case, Dampier's best choices are to Hong Kong,
Taiwan, and Thailand, but due to its disadvantaged position, Dampier will be required to
swing a lower commodity price of $0.45 to $0.56 per mmbtu to lure a potential LNG
venture.
127
Plant Output 6 Equity Portion 1 Interest Rate 0 Commodity Cost (Alt)ITax Rate 0.4 BasePriceFraction 1 Discount Rate
0.1
Period 20
Commidity Price Required to Match the Import Markets' Best NPV
Under Base Case Conditions
$1.00
$0.90
$0.80
m $0.70
E
E
$0.60
Export Terminal
0 Trinidad
$0.50
$0.40
E
E $0.30
0
$0.20
$0.10
$0.00
-E
8~ <
o~~~~~
TOw0,*a
u-CuuC
Cuu
Cu
--0-
-
D
O
C1 w MCW
S
-CuN
~
~
a8
i
0.
0
fu
(D.
0
0
(
V;
cE8
<
-~~C
Z
ON
Import Terminal
Figure 76 - Commodity price required by Trinidad to match each import market's best NPV source
option
Plant Output 6 Equity Portion1 IInterest Rate o Commodity Cost (All) Tax Rate 0.4 BasePriceFraction 1 Discount Rate 0.1 Period 20
Commidity Price Required to Match the Import Markets' Best NPV
Under Base Case Conditions
$1.00
$0.90
$0.80
t $0.70
E
a $0.60
Export Terminal
0lDampier, Australia
$0.50
$0.40
E
E $0.30
0
$0.20
$0.10
(M
-
V
Cu
C4
CCu
Cu C C Cu
2L.
0C
0
0
<
<
C
8
CO
C Cu
Cu
C0cc
.
M
S
.C
x
C
0
CO
M~
E
u
>2
u<
0
CO
>
L
V)
)
<
C.
CO
2
R
z
w
2E
U
8
E
9
C0
Lu
*
$0.00
Uu
M
N
jImport Tria
Figure 77 - Commodity price required in Dampier, Australia to match all import markets' best NPV
source
128
Chapter 9 - Recommendations for Model and Analysis Advancement
The model developed for this research is a grassroots project and was designed
without the benefit of previous attempts of transportation analysis of this type or scale.
While the author is proud of the model's capabilities for conceptual analysis, he
recognizes that time, work, and availability of information could easily yield a more
useful tool. Goals of achieving greater accuracy on the conceptual level, a broader range
of reportable metrics, better usability, and providing a level of decision optimization
stretching into detailed transportation simulations are certainly within the realm of
possibility. This chapter discusses the recommendations that may bring these goals
within reach. Recommendations are categorized in terms of informational inputs, model
capabilities, data management, and ease of use.
Informational Inputs
The model's results can only be as accurate as the information fed into it, and
those results are most affected by the data estimating the size of the initial investment.
The method implemented for estimating the cost of liquefaction and regasification could
use improvement. Instead of the normalized approach for the entire plant, the same
approach to a breakdown of the plant costs may be more appropriate. For a liquefaction
plant, this might include cost breakdowns for each production train, LNG storage tanks,
vessel piers and breakwaters, engineering, construction insurance, and real estate. All of
these specific costs could be affected by a different combination of variables including
length of trade route, number of ships in the trade, plant output, and host nation.
Operating costs could be broken down similarly.
129
Updated tanker information would also make a difference. Research into the
relationships amongst capacity, speed, horsepower, and newbuilding price would
improve the shipping optimization. Conceptual cost and operating information about
much larger tankers would improve the model's overall attractiveness.
With exception to prices from Japan and Korea, contract prices used in the base
case do not stem from LNG data. Nor do these prices reflect any risk on the side of the
buyer for committing to the LNG project. This indicates that actual contract prices would
be somewhat lower. Better pricing data would be useful here.
Calculation Improvements
Combined with more information regarding liquefaction plant build-up schedules
for projects over 10mmtpa, the build-up calculation may require refinement. It is the
author's opinion that the current build-up calculation may be too conservative for large
projects.
The cash flow analysis would be made more realistic with interest calculations.
This would be enhanced if there was also a means to vary the fraction of equity
financing.
A simulation of changes to contract price would also be effective if they could be
linked to the inputs of the pricing structure. Such inputs may include Henry Hub natural
gas prices, Brent oil prices and/or other related publicly traded commodities and indexes.
Allowance for vessel delays within the shipping optimization would add more
reality. This is especially true for large projects that take place over a relatively short
trade route.
130
Currently, the model does not account for overcapacity in a shipping fleet that
serves one project. In reality, it may be possible to charter spot cargos for the surplus
shipping capacity. This flexibility could be added to the model.
The model does not take into account costs and revenues that exist from the
operation of condensate and LPG sales. A simultaneous and/or integrated optimization
would help to better reflect reality.
Detailed results from the local optimizations could be added to the current model
output. In addition to the standard metrics, optimized fleet size, vessel specifications, and
LNG storage capacity could be provided. Other metrics such as return on capital,
payback period, or benefit-cost ratio could also be provided.
Data Management
Improvements in handling results could take many forms. To reduce calculations,
the current import-export matrix could be replaced by an extensive contract pricedistance matrix. Trade route matches could then be sorted from the results of the contract
price-distance matrix. If capital expenses are optimized for specific import and/or export
locations, the matrix simplification would be much more complicated.
Regardless of the manner in which results from each run are presented, an
efficient, automated manner of running tests, warehousing, and accumulating result data
should be devised. The immediate benefit of this approach is the potential of developing
a multi-dimensional test matrix. If the new test matrix could be properly integrated with
model automations, not only could runtime be reduced but a larger test matrix could be
pursued. Currently, testing can only be described as a series of one-dimensional arrays
which takes over 6 hours to run completely while requiring constant manual supervision.
131
User Aspects
The complete model's present design requires four distinct sets of procedures for
setting test parameters, obtaining results, managing data, and analyzing data. In fact, this
requires starting in Microsoft Excel for the first two sets of procedures, using Microsoft
Access for data management, and exporting the data to another Excel file for analysis.
Although these steps have been simplified, they are likely to cause problems with
inexperienced users. The model's marketability as a product would be greatly enhanced
if the entire model ran from a single platform with straightforward user interfaces and
locks on formulas.
132
Chapter 10 - Conclusions
The model developed for this thesis provides the tools for gauging the financial
viability of an LNG project. In fact, the model can be used to show, as in Figure 78, that
there are numerous potential LNG projects that can provide suitable returns. However,
conceptual financial metrics are not the only indicator of project feasibility.
Best Supply Routes
Under Base Case Conditions
-
16%
15% -4-- 14% --
-
13%
E 12%11%
I I
<0
<<
UBeC
0
2
T
<(
(UU
T
U
.-
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<
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C0
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VDa 0 2d
0EU0a f r .In C esa
sezu
.)
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00
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t
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there
r-
m
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cZ
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(U
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)
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00
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U
10%
Figure 78 -The 30 Best Supply Routes in Terms of IRR Under Base Case Conditions
In order for an LNG supply chain to exist, there must be suitable demand for the
suc as th US, Inia Thid an ChDMimm(na An esi prdcto shu
be abl to abor
imported gas and there must be an adequate gas source to draw from. In Chapters 2 and
4 it was determined that natural gas consumption is on the rise. Table 14 summarizes the
forecasted data for selected countries. For nearly all of these countries, imported natural
gas is going to be required to meet some or most of the future needs. In some countries
such as the US, India, Thailand, and China, domestic production should be able to absorb
some of the growth; however, this may jeopardize long-term domestic supplies and may
133
incur higher costs as more difficult reserves are tapped. Some countries faced with a
dwindling domestic gas supply such as the UK may look to imports as a means of
supplying more than just growing demand. Assuming the economics are superior to
pipeline import projects, can LNG suppliers keep up with this growth? Does growth
potential dictate which supply routes are optimal?
Table 14 - Natural Gas Consumption Estimates of Selected Countries. Units: trillion cubic feet per
year
-
Annual Consumption, TCF
2010
2020
2001
32.1
27.1
22.6
4.3
2.4
1.4
4.8
3.7
3.3
0.55
2.6
1.6
1.5
0
0.23
0.08
1.8
1.3
0.6
3.4
2.7
2.5
0.07
-
Country
USA
Mexico
UK
Belgium
France
Portugal
Spain
Italy
Greece
2.8
3.2
3.4
South Korea
China
Taiwan
Thailand
India
0.7
1
0.2
0.7
0.8
1.2
2.3
0.7
1.1
1.8
1.7
4.5
-
Japan
2.0
3.1
An adequate LNG supply source can provide gas to a project for at least 20 years,
and considering the nature of the investment, a 20 year period is conservative. From the
source countries discussed so far,
134
Table 15 displays the calculated available gas reserves from selected source countries
(and Alaska) in 2030 assuming that current production rates continue. There must be
enough remaining gas reserves in 2030 as currently planned, to justify a 20-year LNG
supply chain commissioned in 2010. How does this dictate our choice of supply routes?
135
Table 15 - Estimate of Natural Gas Reserves in 2030 without Existence of Additional LNG Exports
[IEA (2), 20021
Year:
2001
2001
2010
2030
2030
2030
Country/State
Reserves
bcm
Production
bcm
Reserves
bcm
Reserves
bcm
Reserves
TCF
Reserves
mmt LNG
Brazil
232
8
163
9
0.3
6
Brunei
Oman
Peru
Trinidad
Indonesia
Algeria
Norway
Australia
Nigeria
Venezuela
UAE
Alaska
Qatar
Iran
Russia
366
605
260
705
3790
4520
4017
3530
3570
4163
5550
7137
14443
26000
55977
10
17
1
13
67
85
57
33
16
29
41
12
32
61
719
273
450
252
587
3190
3760
3508
3232
3426
3899
5185
7027
14153
25452
49508
68
106
235
325
1857
2070
2375
2571
3107
3313
4374
6784
13508
24235
35132
2
4
8
11
66
73
84
91
110
117
154
239
477
855
1240
47
73
162
224
1278
1424
1634
1769
2137
2279
3010
4667
9293
16673
24171
Options for the US
US forecasted consumption growth between 2010 and 2020 is by far the largest
with 2020 being 5 TCF higher than 2010. Assuming that consumption demand is split
between East and West Coasts, how could the US use LNG to make up this difference?
On the East Coast, Trinidad and Venezuela are the most favorable suppliers with Brazil
and Peru not far behind. Based on information in Table 15, Trinidad would only be able
to supply 0.55 TCF of additional gas per year (i.e. 11 TCF/20 years) which would
exhaust the island's gas reserves by the end of 20 years. Venezuela would be able to
supply the full 50 TCF over 20 years required by the East Coast ports. However, in the
case of Venezuela, the buyer and LNG venture assume significant security of supply risk
due to continued political instability within Venezuela. A diversified supply from Brazil,
Peru, Trinidad, and Venezuela would still require importing 1.6 TCF per year or
136
30.9mmtpa LNG from Venezuela while exhausting supplies from the others. The next
best options are transatlantic supply routes from Algeria and Norway. These routes have
comparable returns while serving to diversify supply risk.
On the US West Coast, Alaska is the best option followed by Peru. Alaskan
reserves could satisfy the 2.5 TCF annual requirements for 100 years. Unfortunately,
expanding gas drilling in Alaska has been set back by restrictions and red tape.
Furthermore, the introduction of a domestic LNG supply chain would be faced with
higher costs due to cabotage laws and would be required to compete with options for
Rocky Mountain drilling, Canadian pipeline imports, and the possibility of an Alaskan
Gas Pipeline. The next best options are Peru and Sakhalin, Russia. Peru's reserves
would only be able to produce 0.4 TCF per year for 20 years. Russia, on the other hand,
could easily supply the West Coast ports' requirements or the US's entire growth
requirement for the foreseeable future. Certainly, the potential risks, mostly politically
related, would require further analysis.
Options for the UK
The situation in the UK is a bit more complicated because forecasts predict annual
consumption to increase 1.1 TCF between 2010 and 2020 and domestic reserves may be
nearly exhausted by 2010. Ignoring growth to 2020, exhaustion could require 3.6 TCF
per year of additional imported supply by 2010 or shortly after. The UK's option with
the best return is to develop an LNG supply route with Norway. Norway could supply
the UK at an additional rate of 4.2 TCF for 20 years before exhausting their reserves.
Not only is this not enough gas to meet 2020 growth projections, but the UK may have to
compete with the US, Belgium, and France for Norwegian gas. The next best alternative
137
is to develop a supply chain with Russia through St. Petersburg to take advantage of
Russia's virtually limitless reserves. Considering potential political risks with Russia, the
UK should evaluate a more diversified portfolio of Norwegian and Russian gas.
Options for China
Although China is projected to experience the greatest relative growth in gas
consumption, their situation is aided by substantial domestic reserves. However,
expansive internal infrastructure is necessary in order to draw completely from their
domestic reserves. LNG may be the temporary solution although "temporary" may last
for more than 20 years. The most favorable LNG return is with a supply chain drawing
from Sakhalin, Russia. Considering the amicable relationship shared by the two
neighbors, this is China's best option. Supply chains from Brunei and Indonesia also
show good returns for China; however, Brunei's reserves would not "go far" into China's
growth, and China may have to compete with Thailand, Korea, and Japan for both
country's gas. Indonesia could supply China's growth alone for 20 years, but reserves
may be near depletion afterwards.
Options for Mexico
Mexico is forecasted to have a gap of about 2 TCF between annual consumptions
of 2010 and 2020. Unless proven reserves increase substantially, it seems unlikely that
any growth beyond 2010 could be absorbed with domestic supplies. The model indicates
that Mexico's best LNG import option to Tijuana is from Alaska. If Alaska is not a
realistic option for the reasons discussed in the beginning of this chapter, Peru,
Venezuela, and Trinidad have the next best returns. If political concerns can be
138
conquered, Venezuela would be the best option. Another way of approaching Mexico's
need for LNG is based on Tijuana's location. Located in Baja California, a supply chain
could be sized to meet the regional requirements of northwest Mexico and possibly the
needs of southern California. If supplying northwest Mexico is the only need, a small
supply chain from Peru may be adequate; however, exporting gas to California could be
very profitable and should be served by a large supplier such as Sakhalin, Russia.
Summary of Best Options
The other consuming nations evaluated here in are in a much better position to
negotiate LNG imports. Many are Mediterranean and Southeast Asian countries that are
forecasted to experience much less growth and are located near several adequate
suppliers.
139
Table 16 lists the import locations and their best match(es) of supply terminal after
considering model results, consumption growth potential, reserve base, and risk.
140
Table 16 - Import Locations Matched with Optimal Supply Locations by Considering Financial
Metrics, Demand Forecasts, Supply Estimates, and Risks Associated with Security of Supply
Import Location
Boston, MA
Baltimore, MD
Savannah, GA
Lake Charles, LA
Tijuana, Mexico
Los Angeles, CA
San Francisco, CA
Seattle, WA
UK
Zeebruge, Belgium
Montoir, France
Portugal
Fos, France
Barcelona, Spain
La Spezia, Italy
Piraeus, Greece
Tokyo, Japan
South Korea
Shanghai, China
Taiwan
Hong Kong
Thailand
Mumbai, India
Supply Location
Algeria or Norway
Algeria or Norway
Algeria or Norway
Algeria or Norway
Peru or Sakhalin, Russia
Sakhalin, Russia
Sakhalin, Russia
Sakhalin, Russia
Norway or St. Petersburg, Russia
Norway
Norway
Algeria or Norway
Algeria or Novorossisk, Russia
Algeria or Novorossisk, Russia
Algeria or Novorossisk, Russia
Algeria or Novorossisk, Russia
Sakhalin, Russia
Sakhalin, Russia
Sakhalin, Russia
Sakhalin, Russia
Sakhalin, Russia
Brunei or Indonesia or Australia
Oman or UAE or Qatar or Iran
141
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146
Appendix
147
Table 17 - Model Results for Five Best Trade Routes. Based on IRR Under Base Case Conditions
Import Location
Baltimore, MD
1" Choice
Trinidad
2 nd Choice
Maracaibo, Venezuela
15.6%
Novorossisk, Russia
3'd Choice
Callau, Peru
14.6%
Stavanger, Norway
4t Choice
Arzew, Algeria
14.2%
St Petersburg, Russia
5" Choice
Recife, Brazil
14.2%
Lagos, Nigeria
IIRR
Barcelona, Spain
15.6%
Arzew, Algeria
IRR
Boston, MA
IRR
Fos, France
IRR
HongKong
IRR
La Spezia, Italy
IRR
Lake Charles, LA
8.8%
Trinidad
15.5%
Arzew, Algeria
3.3%
7.0%
Maracaibo, Venezuela
15.5%
Novorossisk, Russia
2.1%
6.6%
Arzew, Algeria
14.5%
Stavanger, Norway
1.4%
6.1%
Stavanger, Norway
14.3%
St Petersburg, Russia
0.6%
5.6%
Callau, Peru
14.3%
IBrunei
12.7%
Arzew, Algeria
7.3%
Maracaibo, Venezuela
Bontang, Indonesia
12.7%
Novorossisk, Russia
6.2%
Trinidad
Sakhalin, Russia
12.2%
Stavanger, Norway
5.4%
Callau, Peru
Dampier, Australia
11.9%
St Petersburg, Russia
4.9%
Recife, Brazil
IRR
Los Angelos, CA
11.1%
Kenai, Alaska
10.6%
Callau, Peru
10.1%
Maracaibo, Venezuela
9.1%
Trinidad
IRR
Montoir, France
Mumbai, India
11.1%
Stavanger, Norway
3.3%
Muscat, Oman
10.1%
St Petersburg, Russia
2.6%
Abu Dhabi, UAE
10.0%
Arzew, Algeria
2.4%
Ras Laffan, Qatar
IRR
Piraeus, Greece
IRR
Portugal
13.7%
Novorossisk, Russia
7.3%
Arzew, Algeria
IRR
7.3%
Kenai, Alaska
11.6%
Trinidad
13.6%
Arzew, Algeria
7.0%
Stavanger, Norway
6.4%
Callau, Peru
9.8%
Maracaibo, Venezuela
13.6%
Muscat, Oman
5.0%
St Petersburg, Russia
5.7%
Maracaibo, Venezuela
9.5%
Callau, Peru
9.5%
Novorossisk, Russia
0.6%
Bushire, Iran
13.4%
Stavanger, Norway
4.9%
Novorossisk, Russia
5.6%
Trinidad
9.2%
Recife, Brazil
13.8%
Sakhalin, Russia
7.8%
Brunei
12.0%
Brunei
11.7%
Brunei
12.5%
Bontang, Indonesia
13.3%
Callau, Peru
12.6%
Callau, Peru
12.1%
Maracaibo, Venezuela
Tijuana, Mexico
13.8%
Kenai, Alaska
10.8%
Sakhalin, Russia
13.0%
Sakhalin, Russia
13.4%
Sakhalin, Russia
12.7%
Brunei
13.3%
Kenai, Alaska
Lagos, Nigeria
4.9%
Sakhalin, Russia
9.1%
Arzew, Algeria
12.0%
Trinidad
7.1%
Bontang, Indonesia
12.0%
Bontang, Indonesia
11.7%
Bontang, Indonesia
12.5%
Dampier, Australia
12.1%
Maracaibo, Venezuela
IRR
Tokyo, Japan
IRR
UK
IRR
ZeebrugeBelgium
IRR
11.0%
Sakhalin, Russia
1:2.9%
Stavanger, Norway
11.8%
Stavanger, Norway
7.9%
10.3%
Brunei
11.7%
St Petersburg, Russia
11.5%
St Petersburg, Russia
7.5%
10.2%
Bontang, Indonesia
11.7%
Arzew, Algeria
10.6%
Arzew, Algeria
6.7%
7.0%
Dampier, Australia
11.3%
Dampier, Australia
11.2%
Dampier, Australia
11.9%
Muscat, Oman
11.6%
Trinidad
9.8%
Dampier, Australia
10.9%
Novorossisk, Russia
9.3%
Novorossisk, Russia
5.3%
6.8%
Kenai, Alaska
10.6%
Kenai, Alaska
10.3%
Muscat, Oman
10.9%
Abu Dhabi, UAE
11.3%
Sakhalin, Russia
8.6%
Kenai, Alaska
10.7%
Trinidad
8.7%
Trinidad
4.6%
IIRR
San FranciscoCA
IRR
Savannah, GA
IRR
Seattle, WA
IRR
Shanghai, China
IRR
South Korea
IRR
Taiwan
IRR
Thailand
IRR
Lagos, Nigeria
0.1%
Muscat, Oman
11.1%
Muscat, Oman
4.3%
Arzew, Algeria
8.1%
Sakhalin, Russia
8.6%
Trinidad
-0.1%
Brunei
11.8%
Abu Dhabi, UAE
4.9%
148
Table 18 - Model Results for Five Best Trade Routes. Based on Highest NPV Under Base Case
Conditions
Import Location
Baltimore, MD
Choice
Trinidad
1s
$
Barcelona, Spain
Boston, MA
$
$
(1,061,999,095)
Arzew, Algeria
226,481,625
Kenai, Alaska
$
239,726,178
Stavanger, Norway
$
(1,063,298,291)
Mumbai, India
$
765,273,609
$
(471,027,233)
Arzew, Algeria
$
(469,334,589)
San FranciscoCA
328,847,969
Maracaibo, Venezuela
$
Seattle, WA
$
Shanghai, China
811,276,766
Kenai, Alaska
155,017,838
Sakhalin, Russia
$
South Korea
627,540,706
Sakhalin, Russia
$
Taiwan
694,196,528
Sakhalin, Russia
$
568,152,728
Tijuana, Mexico
672,973,971
$
212,598,498
466,096,124
Stavanger, Norway
$
(842,439,846)
$
957,239,169
St Petersburg, Russia
(741,040,400)
$
Stavanger, Norway
$
Lagos, Nigeria
(835,236,585)
Callau, Peru
$
985,289,257
$
24,455,881
990,318,060
St Petersburg, Russia
(1,550,102,862)
$
Lagos, Nigeria
$ (1,652,581,319)
Dampier, Australia
$
410,970,753
Muscat, Oman
$
St Petersburg, Russia
$
(944,847,327)
Callau, Peru
$
Muscat, Oman
$ (1,054,953,043)
Recife, Brazil
$
(192,889,895)
231,579,096
Arzew, Algeria
(405,958,243)
$
Trinidad
(105,746,623)
$
Novorossisk, Russia
Sakhalin, Russia
$ (294,015,998)
Trinidad
$
$
$
$ (1,671,998,163)
(1,170,330,345)
(1,230,548,981)
Abu Dhabi, UAE
$
750,801,203
Arzew, Algeria
(525,214,186)
$
Stavanger, Norway
$
(639,537,574)
Callau, Peru
$
735,139,426
Muscat, Oman
(904,898,053)
$
St Petersburg, Russia
$
(771,735,967)
Maracaibo, Venezuela
$
(105,758,414)
Trinidad
811,276,766
$
Sakhalin, Russia
$
Callau, Peru
565,386,552
Callau, Peru
$
$
(593,777,869)
$
(34,085,223)
(452,295,232)
Bontang, Indonesia
$
421,446,126
361,396,120
$
528,049,677
672,973,971
Callau, Peru
$
53,209,014
421,446,126
361,396,120
$
528,049,677
441,107,456
$
37,462,070
$
598,538,293
Stavanger, Norway
348,286,251
$
Stavanger, Norway
$
361,982,981
St Petersburg, Russia
$
288,594,202
St Petersburg, Russia
Brunei
$
361,982,981
Arzew, Algeria
$
118,816,716
Arzew, Algeria
$
$
$
(439,442,641)
$
$
(171,783,231)
(611,914,812)
$
(944,886,033)
Sakhalin, Russia
(196,528,082)
Recife, Brazil
463,831,870
$
Maracaibo, Venezuela
Arzew, Algeria
$
429,769,323
Trinidad
$
$
(617,586,585)
(679,173,169)
Dampier, Australia
$
278,316,912
Kenai, Alaska
$
Dampier, Australia
$
265,633,424
$
398,681,462
Muscat, Oman
$
340,284,813
Trinidad
$
(33,653,820)
Dampier, Australia
$
185,948,095
Novorossisk, Russia
$
(150,252,941)
Novorossisk, Russia
(874,067,024)
$
132,944,391
Kenai, Alaska
$
Dampier, Australia
Maracaibo, Venezuela
Bontang, Indonesia
386,013,881
$
Abu Dhabi, UAE
$ (945,127,733)
Lagos, Nigeria
Dampier, Australia
$
694,196,528
Bontang, Indonesia
Stavanger, Norway
(940,224,177)
$
Novorossisk, Russia
$
(806,571,698)
Trinidad
Brunei
Brunei
$
$
Brunei
$
Bontang, Indonesia
$
Bushire, Iran
Brunei
Bontang, Indonesia
$
(1,568,419,485)
Ras Laffan, Qatar
Sakhalin, Russia
(375,350,632)
957,558,966
Maracaibo, Venezuela
4,804,356
$
Arzew, Algeria
Kenai, Alaska
Tokyo, Japan
128,930,140
$
5 I Choice
Recife, Brazil
Callau, Peru
23,348,646
$
St Petersburg, Russia
Bontang, Indonesia
$
(682,996,848)
4I Choice
Arzew, Algeria
Sakhalin, Russia
Trinidad
$
Kenai, Alaska
$
(1,415,466,910)
$
Novorossisk, Russia
$
Novorossis-k, Russia
Portugal
559,108,574
1,020,264,786
Stavanger, Norway
$
Bontang, Indonesia
Muscat, Oman
$
Zeebruge,Belgium
(467,342,656)
(1,286,929,064)
Maracaibo, Venezuela
$
UK
559,108,574
1,232,688,283
Novorossisk, Russia
$
Brunei
$
Thailand
1,232,688,283
Arzew, Algeria
La Spezia, lty
Savannah, GA
1,054,009,527
$
$
Piraeus, Greece
$
Stavanger, Norway
$
(638,500,611)
Arzew, Algeria
Hong Kong
Montoir, France
1,270,139,145
Novorossisk, Russia
$
(554,614,179)
Trinidad
$
Los Angelos, CA
$
3r Choice
Callau, Peru
Arzew, Algeria
$
(222,135,155)
Maracaibo, Venezuela
Fos, France
Lake Charles, LA
1,270,139,145
2 nd Choice
Maracaibo, Venezuela
71,859,812
Muscat, Oman
$
194,065,120
Abu Dhabi, UAE
$
290,266,939
Sakhalin, Russia
$
(301,611,508)
Kenai, Alaska
$
152,142,003
Trinidad
$ (271,718,814)
Trinidad
$ (1,002,297,685)
149
Table 19 - Model Results for Five Best Trade Routes. Based on Lowest CapEx Under Base Case
Conditions
Import Location
Baltimore, MD
Barcelona, Spain
Boston, MA
Fos, France
Hong Kong
La Spezia, Italy
Lake Charles, LA
Choice
Trinidad
$
3,112,731,636
Arzew, Algeria
$
2,754,827,771
Maracaibo, Venezuela
$
3,112,795,928
Arzew, Algeria
$
2,870,578,850
Brunei
$
3,089,327,738
Arzew, Algeria
2,870,577,887
$
Maracaibo, Venezuela
1
$
Los Angelos, CA
Montoir, France
Mumbai, India
Piraeus, Greece
Portugal
3,079,378,776
Kenai, Alaska
$
3,240,235,567
Stavanger, Norway
$
2,870,570,937
Muscat, Oman
$
2,870,572,038
Novorossisk, Russia
2,870,559,239
$
Arzew, Algeria
$
2,870,568,184
San Francisco, CA
Kenai, Alaska
Savannah, GA
Maracaibo, Venezuela
$
3,056,826,592
Kenai, Alaska
$
2,912,445,883
Sakhalin, Russia
$
3,016,547,952
Sakhalin, Russia
$
2,942,498,496
Sakhalin, Russia
$
3,084,210,872
Bontang, Indonesia
$
2,955,821,723
Kenai, Alaska
$
3,270,305,392
Sakhalin, Russia
$
Seattle, WA
Shanghai, China
South Korea
Taiwan
Thailand
Tijuana, Mexico
Tokyo, Japan
$
UK
Zeebruge, Belgium
3,100,444,891
2,965,831,490
2 Choice
Maracaibo, Venezuela
$
3,112,731,636
Novorossisk, Russia
$
3,112,731,043
Trinidad
$
3,112,795,928
Novorossisk, Russia
$
3,102,205,979
Bontang, Indonesia
$
3,089,327,738
Novorossisk, Russia
$
3,084,211,498
Trinidad
$
3,144,442,658
Callau, Peru
$
3,468,940,033
St Petersburg, Russia
$
2,960,709,727
3 Choice
Callau, Peru
$
3,353,083,794
Stavanger, Norway
$
3,159,456,892
Arzew, Algeria
$
3,429,823,722
Stavanger, Norway
$
3,246,311,673
Sakhalin, Russia
$
3,151,775,980
Stavanger, Norway
$
3,266,787,038
Callau, Peru
$
3,298,392,821
Maracaibo, Venezuela
$
3,480,036,894
Arzew, Algeria
$
3,043,541,270
4 1h Choice
Recife, Brazil
$
3,480,036,413
St Petersburg, Russia
$
3,314,861,404
Stavanger, Norway
$
3,448,355,134
St Petersburg, Russia
$
3,333,062,554
Dampier, Australia
$
3,290,781,872
St Petersburg, Russia
$
3,429,825,306
Recife, Brazil
$
3,533,904,932
Trinidad
$
3,586,316,544
Novorossisk, Russia
$
3,451,289,589
Abu Dhabi, UAE
2,888,554,675
Arzew, Algeria
$
2,931,075,071
Stavanger, Norway
Ras Laffan, Qatar
2,909,408,487
Muscat, Oman
$
3,333,064,380
St Petersburg, Russia
Bushire, Iran
$
2,942,498,496
Stavanger, Norway
$
3,408,845,309
Novorossisk, Russia
5 1h Choice
Arzew, Algeria
$ 3,480,037,643
Recife, Brazil
$ 3,468,940,636
Callau, Peru
$ 3,468,832,114
Muscat, Oman
$ 3,498,930,659
Muscat, Oman
$ 3,468,939,320
Muscat, Oman
$ 3,510,029,038
Arzew, Algeria
$ 3,724,968,142
Sakhalin, Russia
$ 3,740,331,138
Trinidad
$ 3,510,033,046
Bontang,
Indonesia
$ 3,290,780,096
Abu Dhabi, UAE
$ 3,429,823,540
Recife, Brazil
$
$
$
$
$
3,043,541,270
Callau, Peru
$
3,521,682,976
$
3,144,441,614
Maracaibo, Venezuela
$
3,586,316,486
3,259,576,824
Trinidad
$
3,647,012,531
3,342,839,995
Sakhalin, Russia
$
3,683,002,657
Trinidad
$
3,056,826,592
Sakhalin, Russia
$
3,546,706,219
Bontang, Indonesia
$
3,270,304,104
Bontang, Indonesia
$
3,290,867,265
Brunei
$
3,106,461,494
Brunei
$
2,955,821,723
Callau, Peru
$
3,468,832,114
Bontang, Indonesia
Callau, Peru
$
3,299,746,316
Callau, Peru
$
3,696,321,433
Brunei
$
3,270,304,104
Brunei
$
3,290,867,265
Bontang, Indonesia
$
3,106,461,494
Dampier, Australia
$
3,223,584,492
Maracaibo, Venezuela
$
3,478,346,696
Brunei
Recife, Brazil
$
3,478,346,696
Maracaibo, Venezuela
$
3,710,304,238
Dampier, Australia
$
3,353,081,802
Dampier, Australia
$
3,448,353,980
Dampier, Australia
$
3,283,569,501
Muscat, Oman
$
3,333,063,962
Trinidad
$
3,521,685,063
Dampier, Australia
Arzew, Algeria
$ 3,498,932,028
Trinidad
$ 3,838,253,076
Kenai, Alaska
$ 3,546,707,463
Kenai, Alaska
$ 3,646,881,768
Muscat, Oman
$ 3,510,033,420
Abu Dhabi, UAE
$ 3,451,290,403
Sakhalin, Russia
$ 3,810,302,563
Kenai, Alaska
$
$
$
$
3,270,305,392
3,270,305,392
3,478,345,543
3,498,932,028
Stavanger, Norway
$
2,870,583,323
Stavanger, Norway
St Petersburg, Russia
$
2,926,489,631
St Petersburg, Russia
Arzew, Algeria
$
3,094,735,666
Arzew, Algeria
Novorossisk, Russia
$
3,459,816,997
Novorossisk, Russia
Recife, Brazil
$ 3,533,903,404
Recife, Brazil
$
$
$
$
$
2,870,583,323
2,926,489,631
3,094,735,666
3,459,816,997
3,533,903,404
150
Table 20- Model Results for Five Best Trade Routes. Based on Highest Net Income Under Base Case Conditions
Import Location
It Choice
Baltimore, MD
Trinidad
$
Arzew, Algeria
Barcelona, Spain
$
$
$
$
348,290,026
500,608,432
$
519,453,473
[ndia
242,305,907
57:2,098,080
Portugal
347,642,259
Arzew, Algeria
$
347,940,332
San FranciscoCA
Kenai, Alaska
Savannah, GA
Maracaibo, Venezuela
$
$
521,810,967
603,000,848
$
$
Shanghai, China
464,680,369
Sakhalin, Russia
$
South Korea
564,759,366
Sakhalin, Russia
$
Taiwan
566,967,447
Sakhalin, Russia
$
Thailand
562,079,867
Brunei
$
Tijuana, Mexico
564,607,052
$
518,325,744
Sakhalin, Russia
$
552,102,334
Stavanger, Norway
$
495.,589,365
Stavanger, Norway
$
364,646,608
686,994,424
234,460,078
561,197,012
673,790,343
Stavanger, Norway
$
357,808,933
674,193,673
Stavanger, Norway
$
229,463,972
Sakhalin, Russia
$
553,236,091
Novorossisk, Russia
$
$
339,492,674
492,210,127
Callau, Peru
$
501,423,623
Arzew, Algeria
$
236,735,513
Abu Dhabi, UAE
$
571,379,004
Arzew, Algeria
$
345,442,834
Stavanger, Norway
$
341,876,805
Callau, Peru
$
497,253,070
$
603,000,848
$
425,770,391
$
555,479,225
$
547,706,738
$
558,104,937
Bontang, Indonesia
$
564,607,052
Callau, Peru
$
506,670,992
$
544,900,758
$
490,803,737
$
360,213,526
324,867,173
331,973,313
496,811,042
585,154,218
423,176,598
Brunei
$
555,479,225
Brunei
$
547,706,738
Brunei
$
558,104,937
553,443,382
$
504,969,889
Brunei
$
544,900,758
Arzew, Algeria
$
483,634,679
Arzew, Algeria
$
353,633,155
672,430,764
218,397,763
551,919,856
324,847,333
Recife, Brazil
$
Lagos, Nigeria
349,770,357
$
Stavanger, Norway
671,009,045
$
Lagos, Nigeria
$
213,895,677
Muscat, Oman
538,454,812
$
470,423,589
Trinidad
$
496,813,141
Novorossisk, Russia
$
222,326,172
Bushire, Iran
$
566,967,447
Abu Dhabi, UAE
$
324,799,918
316,081,279
Stavanger, Norway
466,091,617
$
Trinidad
$
Sakhalin, Russia
481,954,145
$
Maracaibo, Venezuela
212,417,300
$
Bontang, Indonesia
$
551,848,777
Stavanger, Norway
323,171,785
$
Trinidad
Novorossisk, Russia
$
331,721,295
$
492,362,055
Sakhalin, Russia
$
Recife, Brazil
$
580,794,865
Trinidad
$
421,851,304
540,822,261
542,132,561
Maracaibo, Venezuela
$
553,160,847
Abu Dhabi, UAE
$
546,802,272
$
497,330,007
531,376,306
Muscat, Oman
$
469,446,027
$
340,723,994
545,268,594
Sakhalin, Russia
$
485,826,742
Kenai, Alaska
527,742,820
$
Lagos, Nigeria
$
Novorossisk, Russia
$
536,480,965
Ras Laffan, Qatar
Novorossisk, Russia
$
535,470,088
$
Dampier, Australia
$
529,848,678
Kenai, Alaska
Trinidad
$
420,672,729
Kenai, Alaska
Dampier, Australia
$
577,115,655
$
Dampier, Australia
$
492,197,452
Arzew, Algeria
Dampier, Australia
$
326,378,402
$
Trinidad
Maracaibo, Venezuela
$
354,492,768
668,786,821
St Petersburg, Russia
Dampier, Australia
$
$
Dampier, Australia
$
Callau, Peru
$
St Petersburg, Russia
$
$
Callau, Peru
St Petersburg, Russia
$
$
Maracaibo, Venezuela
Bontang, Indonesia
$
570,764,909
St Petersburg, Russia
Bontang, Indonesia
$
234,661,139
668,843,808
St Petersburg, Russia
Ras Laffan, Qatar
Bontang, Indonesia
$
499,407,872
51h Choice
Recife, Brazil
Callau, Peru
Ras Laffan, Qatar
Bontang, Indonesia
$
488,522,305
St Petersburg, Russia
Sakhalin, Russia
$
$
Maracaibo, Venezuela
Trinidad
$
330,408,383
4 Choice
Arzew, Algeria
St Petersburg, Russia
Callau, Peru
Trinidad
$
$
Arzew, Algeria
$
Stavanger, Norway
Kenai, Alaska
Tokyo, Japan
$
Brunei
$
Kenai, Alaska
Seattle, WA
366,098,477
Novorossisk, Russia
Novorossisk, Russia
$
Zeebruge,Belgium
$
Muscat, Oman
$
693,650,937
Choice
Callau, Peru
Maracaibo, Venezuela
Stavanger, Norway
$
UK
$
Kenai, Alaska
Los Angelos, CA
3
Choice
Novorossisk, Russia
Maracaibo, Venezuela
$
Piraeus, Greece
561,197,012
Arzew, Algeria
La Spezia, Italy
Mumbai,
242,534,069
$
Bontang, Indonesia
$
Montoir, France
686,994,424
Arzew, Algeria
Fos, France
Lake Charles, LA
381,063,657
Trinidad
Boston, MA
Hong Kong
693,650,937
2
Maracaibo, Venezuela
460,003,538
Lagos, Nigeria
$
331,885,907
151
Table 21 - Distance Between Ports in Nautical Miles (Import Along the Left & Export Along the Right)
Trinidad
Boston,
MA
Arzew,
Algeria
Lagos,
Nigeria
Stavanger,
Norway
Bushire,
Iran
Muscat,
Oman
Abu
Ras
Dhabi,
UAE
Laffan,
Qatar
St
Bontang,
Indonesia
Brunei
Dampier,
Australia
Kenai,
Alaska
Maracaibo,
Venezuela
Recife,
Brazil
Callau,
Peru
Petersburg,
Russia
Sakhalin,
Russia
Novorossisk,
Russia
2004
3306
4980
3450
8149
7563
7817
7920
10450
10450
11200
7250
2004
3670
3500
4300
10250
5300
1920
3750
5064
3940
8577
7991
8245
8348
10700
10700
11530
6900
1920
3773
3200
4800
10000
5666
1590
3800
4800
4064
8700
8114
8368
8471
10831
10831
11650
6700
1590
3600
2900
4990
9650
5830
2180
5020
5950
5110
9650
9064
9318
9687
11780
11780
11600
6570
1700
4070
2800
6010
9350
6900
3950
7450
7855
7650
11676
11090
11344
11447
7290
7290
7880
2500
3600
5800
3500
8640
5200
9550
4114
7600
8005
7800
11609
11023
11277
11380
7300
7300
7890
2350
3764
6172
3650
8790
5150
9561
CA
4465
7900
8337
8100
11095
10509
10763
10866
7640
7640
7645
1895
4115
6500
3990
9060
4650
9900
Seattle,
WA
Baltimore,
MD
Savannah,
GA
Lake
Charles,
LA
Tijuana,
Mexico
Los
Angelos,
CA
San
Francisco,
5224
8700
9113
8980
10804
10218
10472
10575
7350
7350
7550
1000
4874
7280
4746
9880
4200
10673
UK
Zeebruge,
Belgium
Montoir,
France
4020
1850
4300
450
6500
5914
6168
6271
8500
8500
9960
9800
4320
4100
5950
1120
11660
3420
4020
1850
4300
450
6500
5914
6168
6271
8500
8500
9960
9800
4320
4100
5950
1120
11660
3420
3840
1500
3930
630
6100
5514
5768
5871
8400
8400
9300
9700
4140
4000
5990
1330
11650
3330
Portugal
Fos,
France
Barcelona,
Spain
La Spezia,
3319
670
3284
1500
5400
4814
5068
5171
7230
7230
8440
9600
3619
3154
5590
2200
10700
2530
4141
515
3780
2400
4411
3825
4079
4182
6820
6820
7720
10200
4441
3830
6400
3090
10220
1830
2950
10220
1930
Italy
Piraeus,
Greece
Tokyo,
Japan
South
Korea
Shanghai,
China
3926
340
3614
2290
4560
3974
4228
4657
6860
6860
7800
10000
4226
3639
6215
4306
529
4178
2620
4501
3915
4169
4272
6750
6750
7630
10350
4606
4000
6570
3280
10020
1740
4961
1110
4619
3270
3641
3055
3309
3412
5890
5890
6800
11000
5261
4640
7720
3930
9400
800
9206
9500
10893
11520
5749
5163
5417
5520
2500
2500
3620
3800
8856
10960
8575
12477
1350
9290
9373
9050
10357
11500
5299
4713
4967
5070
2840
2840
3470
4300
9023
11436
8825
11220
1200
8800
9858
8800
10201
11000
5049
4463
4717
4820
2520
2520
3240
4175
9508
10947
9550
11790
1400
8550
Taiwan
Hong
Kong
10153
8170
9479
10340
4419
3833
4087
4190
1960
1960
2653
4700
9803
10558
9673
11190
1750
7920
10398
8000
9473
10166
4249
3663
3917
4020
1790
1790
2710
4830
10048
10220
10017
10820
2220
7750
Thailand
Mumbai,
11404
7400
8958
9750
3649
3063
3317
3420
1290
1290
2400
5300
11054
9700
11150
10400
3660
7120
8399
4610
7187
6740
1200
614
868
971
2740
2740
3700
8900
8049
7933
10690
7390
6000
4320
India
152