MINISTRY FOR EDUCATION AND SCIENCE, RUSSIAN FEDERATION
Federal State Autonomous Organization of Higher Education
«Novosibirsk National Research State University»
(NSU)
Faculty of Economics
MASTER PROGRAM IN OIL AND GAS MANAGEMENT
Course paper
Jorge Luis Chacón Solar
Development of an Economical Approach for Investment Optimization in Heavy Oil
Industry
Supervisor:
Semykina, I.O., Cand. Econ.
Silkin V.Yu., Cand. Econ.
Head of the Chair
Svetlana A. Kuznetsova, PhD. Assoc. Prof.
The thesis is accomplished on the Chair of Mathematical Methods in Economics of Economic
Department, NSU
Novosibirsk
2017
2
TABLE OF CONTENT
INTRODUCTION
3
CHAPTER I. UNDERSTANDING THE IMPORTANCE OF HEAVY OIL
6
1.1 ENERGY INVESTMENT
6
1.2 THE ROLE OF HEAVY OIL IN THE FUTURE
6
1.3 CHALLENGES IN HEAVY OIL: EXTRACTION AND TRANSPORTATION
10
1.4 A DIAGNOSIS OF VENEZUELAN OIL INDUSTRY
13
1.5 CANADIAN OIL SANDS
17
1.6 THE RUSSIAN CONTRIBUTION
19
CHAPTER II. METHODOLOGICAL FRAMEWORK
21
2.1 OIL MARKETS AND NATIONAL ECONOMIES
21
2.2 ANALYSIS OF APPROACHES OF INVESTMENT EVALUATION IN OIL INDUSTRY
22
CHAPTER 3. INVESTMENT MODEL
27
3.1 IDENTIFYING THE VARIABLES
27
3.2 BUILDING THE MODEL
35
CONCLUSIONS
50
REFERENCES
52
ANNEX 1
55
3
INTRODUCTION
Heavy oil analysis and modeling is vital for today’s world society. As the global economy
continues to depend on oil and oil derivatives, and taken into consideration the enormous
volatility of oil markets, developing methods to economically optimize investments in the
industry of heavy oil -which has not even reached its potential- appears to be a strategic
matter for companies and nations willing to take advantage on international energy markets.
Conventional oil resources are naturally declining after decades of massive consumption, and
traditionally oil producers such as Iraq, Syria, Iran or Libya are facing colossal security
difficulties, which affect international global market and cause even more volatility than that
already existed. Long run perspectives for oil markets are far away from being clear and
prices equilibrium tend to change constantly, affected not just by internal oil industry
behavior but by exogenous variables. On the other side, reserves of unconventional oil are
enormous worldwide and interest on them increases on a daily basis. Despite the existence of
serious challenges in the sector, problems in heavy oil production are partially solved with the
constantly enhance of new sophisticated recovery methods; however, new technology is still
needed to reduce risks and costs.
Researchers have produced an enormous quantity of investigations on heavy oil,
fundamentally from the perspective of engineering, geology, chemistry or physics; however,
the economic perspective is still to grow and this document expects to contribute to the
increase of academic literature concerning the economical aspect of the sector.
The idea of this paper is topical because heavy oil resources are essential to satisfy global
energy consumption for next decades; moreover, an appropriate economic valuation of them
will encourage large-scale investments which will stimulate economic growth, create jobs,
provide needed energy and generate significant revenues for companies, countries and the
society itself, fulfilling climate change policies at the same time..
The aim of this investigation is to propose an economical approach to take the most optimal
investment decision in heavy oil fields, taking into account key variables such as exchange
rate, oil benchmarks spread, technology, discount rate, capital and operating costs, taxes and
environmental expenses.
Under this frame, the following objectives are assigned:
4
1. To select a specific non-conventional oil field whose characteristics satisfy the
requirements of analyzes: to possess big amount of resources and to be
economically feasible to produce.
2. To analyze investment amounts under two categories: capital and operational
investments.
3. To evaluate the volatility of exchange rate fluctuations among Canadian Dollar
and US Dollar, and the volatility among Brent and WTI benchmarks.
4. Forecast increases in oil price and quantity of oil produced for the next twenty
years.
5. To build an investment model which takes into consideration an extra increase in
production due to technology productivity. Considering the time analyzed, a
twentieth degree model is created.
6. To evaluate the influence of technological annual increases and discount rate
changes on total income and revenues.
7. To calculate how much changes in costs affect total revenues for the project.
8. To estimate the contribution of the oil produced in the project as a percentage of
total global oil demand for different values of annual technological increases.
9. To stablish equilibrium values of technology rate and discount rates, in order to
know how much one variable must change as a result of a change in the other for
maintaining a profitable project.
The method for this research is based on economical, mathematical and statistical
methodologies, as well as sensitivity analyzes. As any investment analysis consists in
forecasting costs and revenues within an intertemporal framework, this document’s subject
assess to develop an empirical methodology which could develop a better system for
increasing companies’ revenues when producing heavy oil restricted to the product and
market conditions. As a consequence, expected results consist in providing a financial model
on how to optimize long run investments in heavy oil industry. For this, economical
modelling will be carried out
In order to do so, one country will be taken into special consideration as the main object of
this research: Canada, due to the existence in it of the second biggest heavy oil reserves in the
world –after Venezuela-. Comparative historical analyses were made and results impulse the
research’s process. In Venezuelan case, a special analysis of its oil industry will be done with
the aim of providing more information about its situation, along with descriptions of deposits
in Canada, US and Russia.
5
Official statistics from different sources will be taken into account. As for information about
the object of study, Canadian public and private national and regional organizations will be
taken into account: As an example: Canadian Energy Research institute (CERI), Alberta
Energy Regulator (AER), Department of Energy (DEO), Alberta Utilities Commission (AUC)
and the Alberta Marketing Petroleum Commission (APMC). World Energy Outlook from the
International Energy Agency (IEA) is also considered. On the other hand scientific articles
and historical stock market data will also be studied. In total, this document contains 38
references without including quotes from reports, studies and outlooks.
Some authors have contributed with outstanding documents to this topic and were specially
taken into consideration. Cortazar, Schwarts and Casassus (2001); Lin and Ji (2007);
Midthun, Fodstad and Hellemo (2015); Qin, Wang and Xue (2015); Baoshend and Qing
(2011) and Ettehadtavakkol, Jablonowski and Lake (2016) are some examples.
Scientific novelty of this research relies on the fact that it provides an investment model that
can be successfully applied to non-conventional oil investment projects under uncertainty.
From a more specific perspective, it shows a methodology to calculate NPV for a specific
investment project on a long-term period under fluctuating variables (i.e. exchange rate, oil
price, oil production, costs) with the inclusion of technology; in addition, it provides values
for technological and discount rate in order to optimize the investment.
In what follows, Chapter I shows a forecast about the amount of money planned to be
invested in the energy sector, explains why is important to take into consideration heavy oil
for the future of global energy consumption, and shows key challenges when producing heavy
oil: extraction and investment; besides, explains the most important difficulties the
Venezuelan oil industry is facing today and also describes Canadian, US and Russian heavy
oil deposits. Literature review related to previous studies on modeling investments in oil
industry and macroeconomic effects of oil prices variations, alongside with current theories
for understanding oil prices volatility, are examined in Chapter II. Chapter III describes the
main variables, presents the investment model and shows the results. Chapter IV offers the
conclusions and finally, an index of references and an annex can be found at the end of the
document.
6
CHAPTER I. UNDERSTANDING THE IMPORTANCE OF HEAVY OIL
1.1 ENERGY INVESTMENT
According to the World Economic Investment Outlook (WEIO) 2014 from the International
Energy Agency, during the 2013 more than $1,600 billion were invested at a global scale to
provide humankind with energy1. Plus, $130 billion was invested in improving energy
efficiency. In the long run, the EIA forecasts the world will require $2,000 billion of yearly
investment in 2035 in order to be provided by energy, along with $550 billion in investment
for improving energy efficiency. In sum, the amount required until that year will reach $48
trillion, consisting of around $40 trillion in energy supply and the rest in energy efficiency.
From this, $23 will be invested in fossil fuel extraction, transport and oil refining, close to $10
trillion in power generation ($6 trillion in low-carbon technologies renewables and $1 trillion
in nuclear as the most important); and $7 trillion in transmission and distribution. About two
thirds of these investments will take place in emerging economies, especially Asia (moving
away from China toward other regions), Africa and Latin America.
The growth of demand will require increasing energy production, for this purpose less than
half of $40 trillion will be invested to meet this growth; the rest will be invested to replace
power plants and other facilities. In a more specifically way, 80% of upstream spending in oil
and gas will be absorbed to compensate output declines. As for the investment in energy
efficiency, 90% of the $8 trillion spent in 2035 will be used in transport and building sectors.
1.2 THE ROLE OF HEAVY OIL IN THE FUTURE
The U.S. Geological Survey (USGS) defines Heavy Oil as an asphaltic, dense, viscous crude
oil, with less than 22° API gravity, a viscosity of less than 100Cp (centipoise) at reservoir
conditions, and contenting of asphaltene (large molecules with sulfur and perhaps 90% of the
metals in the oil)2. Heavy Oil is non-conventional oil, alongside with light tight oil, gas to
liquids, coal to liquids and kerogen oil (World Economic Outlook –WEO-, 2012)3. The
1
World Energy Investment Outlook. Special Report. Published by International Energy Agency. 2014.
www.worldenergyoutlook.org/investment
“What is Heavy Oil and how is it formed?” Rig Zone. Taken 14/05/2016.
http://www.rigzone.com/training/heavyoil/insight.asp?i_id=184
3 World Energy Outlook, 2012. International Energy Agency. Page 100.
2
7
distinction between heavy oil and bitumen is based on the differences of viscosities, not on
differences of gravity or chemical composition. Under reservoir pressure and temperature,
bitumen is more viscous than extra-heavy oil4.
Graph 1
Source: World Economic Outlook –WEO-, 2012, Page 100.
Although extra-heavy oil and bitumen can be found through the entire world5, to this day two
countries hold the position of having the biggest reserve of heavy oil in the world: Venezuela
and Canada6. Both of them have vast heavy oil deposits, considered the most important of the
world, with a magnitude of 3,5-4 Trillion7 barrels of Oil in Place (Bbl. OOIP)8. Through last
decades, enormous efforts have been made in order to increase commercial development of
these fields. In the Canadian case, the most applied technique has been CHOPS9, while in the
Venezuelan case, prior to year 2,000 a Venezuelan-developed technique called Orimulsion
was used, consisting in bending heavy oil (of 9° API gravity) with water in a 70% oil and
30% H2O mix. Later, several techniques have been taken into consideration for the Orinoco
Extra-heavy oils and bitumen. Reserves for the future”. Exploration & Production. The Know-How Series.
TOTAL. 2016. Page 04.
5
Íbidem
6
Other reservoirs can be found in Middle East, Russia, USA, Mexico and Brazil.
4
7
For the purpose of this document, 3,5 Trillion is defined as 3,500,000,000,000 barrels.
“Comparing Venezuela and Canadian Heavy Oil and Tar Sands”. M.B. Dusseault. Canadian International Petroleum
Conference. Page 2. http://www.southportland.org/files/3713/9387/3165/Heavy_Oil_and_Tar_Sands.pdf
9 CHOP is a “primary heavy oil production that involves the deliberate initiation of sand influx into a perforated oil well, and
the continued production of substantial quantities of sand along with the oil, perhaps, for many years”. “Comparing
Venezuela and Canadian Heavy Oil and Tar Sands”. Page. 30.
8
8
Belt, such as cold recovery10 and Steam Assisted Gravity Drainage (SAGD)11, guided
specially by French company Total.
As its shown in Graph 2, to this day extra-heavy oil equals for less than 5% of global oil
production and -under the assumptions that existing policy commitments and new recently
announced will be implemented-12 it will contribute with less than 10% of total in 203513. One
of the reasons why is important to consider heavy oil potential in the close future relies on the
fact that increasing its production will put into energy market vast amounts of oil from
countries such as the US, with fields in California, East Texas, Gulf Coast and Mississippi
Salt Dome14; Canada, with the Alberta Province fields; Venezuela, with the Orinoco Belt or
Russia with the Bazhenov shale15. As a consequence, heavy oil production could drastically
change the energy world map. It could relocate strategical oil regions and influence world
geopolitics. Therefore several companies and governments are taking into serious
consideration the consequences of non-conventional oil resources and its role in defining the
next decades.
Graph 2
Source: WEO, 2012. Page 104
“Total. Strategic Sectors. Extra-Heavy Oil & Oil Sands. The Challenges of Develoment”. October 2012. Page 3.
Extra-heavy oils and bitumen. Reserves for the future”. Exploration & Production. The Know-How Series. TOTAL. 2016.
Page 10.
12 “New commitments include renewable energy and energy efficiency targets, programmes relating to nuclear phase-out or
additions, national targets to reduce greenhouse-gas emissions communicated under the 2010 Cancun Agreements and the
initiatives taken by G-20 and Asia-Pacific Economic Cooperation (APEC) economies to phase out inefficient fossil-fuel
subsidies”. World Economic Outlook (WEO). International Energy Agency. 2012. Page 34
13 World Energy Outlook, 2012. International Energy Agency. Page 104.
14 Meyer, Richard; Attanasi Emil and Freeman Philip. “Heavy Oil and Natural Bitumen Resources in Geological Basins of
the World”. Open File-Report 2007-1084. U.S. Department of the Interior – U.S. Geological Survey. Page 8.
10
11
15
Ulmishek, Gregory. “Petroleum Geology and Resources of the West Siberian Basin, Russia”. U.S. Geological
Survey Bulletin 2201-G. U.S. Department of the Interior - U.S. Geological Survey.
9
Exxon Mobil, in its 2016 Outlook for Energy, expects “most of the growth (in oil production)
through 2040 to come from technology-driven supplies including tight oil, NGLs, oil sands
and deep-water production”16. French Total also expects that heavy oil production will reach
7-8Mb/d by 203017. According to British Petroleum Outlook, by 2035 fossil fuels will provide
80% of total energy supplies, forecasting that non-conventional oil and gas resources could
have even greater potential18.
Importance of heavy oil highlights when taken into consideration royalties generated by its
production. According to the Royalty Review Submission to the Alberta Government
elaborated by Canada’s Oil & Natural Gas Producers in 2015, oil and gas industry is a key
contributor to the Alberta economy, causing benefits to Albertians in the size of $10,7 Billion
in royalties, land-sales, corporate and municipal tax and carbon levy; 20,000 companies from
Alberta signing agreements with the oil and gas industry and creating 375,000 direct and
indirect employments across Alberta19. In addition, it has encouraged investment, optimizing
returns to Albertians, ensuring a responsible development with strong focus on innovation and
diversification.
According to CIA World Factbook, Venezuela owns 298,4 bln oil barrels while Canada owns
172,5 bln oil barrels (these numbers include conventional and non-conventional reserves)20.
The International Energy Agency forecasted world daily oil consumption for 2016 by 96 bln
barrels21, which is equivalent to 35 bln barrels for the entire year. The importance of the
development of heavy oil resources is highlighted when a brief analysis shows that Venezuela
could satisfy 100% of current global oil consumption for 8,5 years while Canada could satisfy
100% of current global oil consumption for 4,9 years. If Venezuela satisfies 20% of current
global oil consumption, it will take 42,5 years until its proved reserves are over, in the case of
Canada it will take 24,61 years until its proved reserves are over. Together, they could fulfill
half of current global oil requirements during 26 years and total global consumption until
2029.
Despite the importance of heavy oil production for the world, in order to successfully release
its potential is necessary to take into consideration aspects such as planned investments in
energy sector through the world for the next decade, dynamics of conventional and other nonconventional oil markets; dynamics of low-carbon and alternative technologies; national and
“The Outlook for Energy: A view to 2040”. Exxon Mobil. 2016. Page 58.
Total. Strategic Sectors. Extra-Heavy Oil & Oil Sands. The Challenges of Develoment”. October 2012. Page 2.
18 British Petroleum Energy Outlook. 20165 Edition. Oultook to 2035. Page 71.
16
17
19
“Royalty Review Submission to the Alberta Government”. Published by Canada’s Oil & Natural Gas
Producers. October, 23. 2015
20
CIA – World Factbook. https://www.cia.gov/library/publications/the-world-factbook/rankorder/2244rank.html
21
World Energy Outlook, 2012. International Energy Agency.
10
global policies and efforts regarding on stronger actions to address climate change; dynamic
of transportation sector –with an emphasis on low-carbon energy systems and fuels; and
industrial, architectural and fuel innovations in order to optimize energy consumption; among
others.
In brief, heavy oil contributes today with less than 5% of total oil production, however a
successfully release of heavy oil potential could supply global oil consumption for the next
decades, changing global energy market map geopolitics and relocating resources into new
regions.
1.3 CHALLENGES IN HEAVY OIL: EXTRACTION AND TRANSPORTATION
As we have seen, Venezuela and Canada have vast heavy oil deposits, considered the most
important of the world22. Due to the large quantities of heavy and extra heavy oil located in
these reservoirs, the importance of these oil belts for global market stability have been taking
into serious consideration -especially during the last decades. The costs associated with the
production of this kind of petroleum are higher than their equivalent in light petroleum,
mainly as a consequence of the intensive use of more expensive technology, more complex
extraction techniques, more expensive refining facilities and transport logistics23. Failure has
been a constant when extracting heavy oil; in Canada for example, there are hundreds of
inactive oil wells with expensive screens and gravel packs24.
In order to satisfy the global demand for oil and create new business opportunities, many big
and small firms have developed several techniques to extract heavy and super heavy oil under
reasonable economic, technical and environmental frameworks. To this day, the most used
Recovery Processes used for heavy and extra-heavy oil are:
A. Primary.
a. CHOP is a “primary heavy oil production that involves the deliberate initiation of sand
influx into a perforated oil well, and the continued production of substantial quantities of
“Comparing Venezuela and Canadian Heavy Oil and Tar Sands”. M.B. Dusseault. Canadian International
Petroleum Conference. Page 2.
http://www.southportland.org/files/3713/9387/3165/Heavy_Oil_and_Tar_Sands.pdf
23
“Cold Heavy Oil Production with Sand in the Canadian Heavy Oil Industry”. PhD. Dusseault, Maurice.
Published on March, 2002. Page 26. http://www.energy.alberta.ca/OilSands/1189.asp
24
“Cold Heavy Oil Production with Sand (CHOP)”. Petrowiki.
http://petrowiki.org/Cold_heavy_oil_production_with_sand
22
11
sand along with the oil, perhaps, for many years”25. It can be defined as a new oil
production technology “because it requires a radically different approach to oil field
management and because scientific and engineering personnel have to learn new
physical principals and apply them26”. Besides, “it is a primary production method
because it exploits natural energy sources in the reservoir: energy from dissolution and
expansion of gas (compressional energy), and energy from the downward motion of the
overburden (gravitational energy)”27.
B. Thermal
a. Steam-Based
i. CSS (Cyclic Steam Stimulation). Better known as Cyclic Steam Injection is a method in
with steam is introduced into the well to produce oil. It is divided into three stages: in the
first stage, steal is injected into the well; later the well is shut down for several days so
the heat can be uniformly distributed; finally, the oil is extracted28.
ii. SAGD (Steam Assisted Gravity Drainage). After digging two shafts with 5 meters of
difference, steam is injected by the upper one heating the oil and making it drain into the
lower one by gravity29. This specific technique has been used by French company Total
in the sands of Zuata field, Venezuela. The reservoir rest from 350 to 600 meters below
the surface under temperatures over 50° C. It could be easily extracted; however the low
gravity (8,3° API) of the crude reduce the productivity, therefore Nafta is added to
increase its gravity up to 17° API gravity. When pulled out, the oil is processed at the
main facility of San Diego de Cabrutica were the crude, gas and water are separated;
before being transported through a 200 km pipe line to the Jose upgrader.
b. Combustion
i. Fire Flooding. In this method a fired is initiated at the sandface of an injection well
creating a flame front. The high temperatures created help the oil to reduce its viscosity
and make it easier to extract30.
ii. THAI (Toe-to-Heel Air Injection). A process in which part of the oil is burned inside the
reservoir using air injected by a vertical well; the heat generated by this combustion front
“Cold Heavy Oil Production with Sand in the Canadian Heavy Oil Industry”. PhD. Dusseault, Maurice.
Published on March, 2002. Page 26. http://www.energy.alberta.ca/OilSands/1189.asp
26
“Comparing Venezuela and Canadian Heavy Oil and Tar Sands”. Page. 30
27
Íbidem. Page. 30
28
Cyclic Steam Injection. Definition. Schlumberger.
http://www.glossary.oilfield.slb.com/Terms/c/cyclic_steam_injection.aspx
29
“Orinoco Oil Belt”. Sojka, Michael. December 16, 2011. Stanford Univeristy.
http://large.stanford.edu/courses/2011/ph240/sojka1/ and Steam-Assited Gravity Drainage. Schlumberger.
http://www.glossary.oilfield.slb.com/Terms/s/steam-assisted_gravity_drainage.aspx
30
Fire Flooding. Definition. Schlumberger. http://www.glossary.oilfield.slb.com/en/Terms/f/fire_flooding.aspx
25
12
reduces the viscosity of oil and allows its extraction through an horizontal well31. The oil
moves from the toe to the heel of the horizontal producing well recovering an estimated
80% of the OOIP.
C. Non-Thermal
a. Water Flooding. Water is injected in a secondary recovery technique in order to sweep
the oil from the well. This might create problems such as water breakthrough32 33.
b. Chemical Flooding. The use of chemical solutions whether to reduce surface tension
between oil and water (such as alkaline substances) or to improve the sweep efficiency
(with elements such as polyacrylamide or polysaccharide)34.
c. VAPEX (Vapor-Assisted Petroleum Extraction) where the viscosity is reduced mixing
hydrocarbons in gravity dominated drainage regime35.
One of the most important techniques is Cold Heavy Oil Production with Sand (CHOPS),
since it’s the most used in Heavy Belt and Oil Sands deposits in Alberta and Saskatchewan,
Canada36. This technique has transformed the heavy oil industry because it increases the
wellbore’s productivity due to four reasons:
1. The basic permeability to fluids is enhanced if sand can move.
2. A growing zone of greater permeability is generated around the wellbore as more sand
is produced.
3. The ex-solution of gas in heavy oil generates a bubble phase37, leading to an internal
gas drive, also called “foamy flow”38.
4. Continuous sanding means that asphaltene or fines plugging of the near-wellbore
environment cannot occur to inhibit oil flow39.
As disadvantages of CHOPS, it must be said that it produces large quantities of oily sand
as well as various categories of fluid wastes (water with enormous amounts of chloride –
31
Extracting Heavy Oil: Using Toe-to-Heel Air Injection (THAITM). Published on August 27, 2007.
http://www.theoildrum.com/node/2907
32
WaterFlooding. Schlumberger. http://www.glossary.oilfield.slb.com/Terms/w/waterflooding.aspx
33
Water Breakthrough is a phenome where water gain access to producing wellbores.
http://www.glossary.oilfield.slb.com/Terms/b/breakthrough.aspx
34
Chemical Flooding. Schlumberger. http://www.glossary.oilfield.slb.com/Terms/c/chemical_flooding.aspx
35
“Comparing Venezuela and Canadian Heavy Oil and Tar Sands”. M.B. Dusseault. Canadian International
Petroleum Conference. Page 5.
36
“Comparing Venezuela and Canadian Heavy Oil and Tar Sands”. Page 2.
http://www.southportland.org/files/3713/9387/3165/Heavy_Oil_and_Tar_Sands.pdf
37
The “bubblepoint pressure” is the pressure at which the natural gas begins to come out of solution and form
bubbles. http://petrowiki.org/Oil_bubblepoint_pressure
38
According to Brij Maini from the Petroleum Recovery Institute, the term “foamy oil” is referred as heavy oil
containing a large volume fraction of very small gas bubbles. https://www.onepetro.org/journal-paper/PETSOC96-06-01
39
“Comparing Venezuela and Canadian Heavy Oil and Tar Sands”. Page 5.
13
dissolved NaCl, water-oil-clay emulsions, and slops, among others). In addition, CHOPS
wells need more workovers than conventional oil wells, increasing the Operating Costs
(OPEX) in about 15%-25%40.
Considering the global voracious appetite for oil, and the necessity of a trustable and
closely oil source for the US and European Markets, the heavy oil will become even more
important in the future. In order to fulfill the demand’s expectative, existing producing
techniques must be enhanced and new techniques must be developed.
1.4 A DIAGNOSIS OF VENEZUELAN OIL INDUSTRY
With 297,7 bln proved barrels, Venezuela has the most important oil reserves of the world,
followed by Saudi Arabia with 268,4 bln barrels and Canada with 173,2 bln barrels41. On
natural gas proved reserves, Venezuela holds the eighth place with 5,562 bln cubic meters42.
Despite having the biggest oil reserves, Venezuela is the twelfth oil producer in the world, the
third in Latin America and the fifth in America, producing 2,475,000 barrels per day43.
This incredible wealth is managed by the 100% stated owned Venezuelan oil national
company, PDVSA (Petróleos de Venezuela or Petroleum of Venezuela), which has the
monopoly of oil production and commercialization (although joint-venture societies with
foreign companies are legally allowed and very used). Being created at 1976, PDVSA is
considered today as one of the most important companies in the world. Fortune ranked it as
the 39th largest corporation in the world44, and it’s one of the largest companies in South
America45.
Despite the potential of the country’s main company to achieve enormous successes in the oil
market, some difficulties have increasingly been threatening the industry, causing several
damages to it, both in the short run and long run.
A Brief Outlook to the Venezuelan Oil Industry
The collapse of the oil barrel price in 2014 found Venezuela in an extremely delicate
economic and political situation. The inflation rate for the 2014 was the highest in the region,
“Comparing Venezuela and Canadian Heavy Oil and Tar Sands”, Page 31
CIA – World Factbook. https://www.cia.gov/library/publications/the-world-factbook/rankorder/2244rank.html
42
CIA – World Factbook https://www.cia.gov/library/publications/the-world-factbook/rankorder/2253rank.html
43
CIA – World Factbook https://www.cia.gov/library/publications/the-world-factbook/rankorder/2241rank.html
44
Ranking the Brands. Fortune Global 500. http://www.rankingthebrands.com/The-BrandRankings.aspx?rankingID=50&
45
Top 10 Companies in South America 2015. http://www.mbaskool.com/fun-corner/top-brand-lists/13965-top10-companies-in-south-america-2015.html?start=9
40
41
14
being more than 60%46, for 2016, International Monetary Fund forecasts inflation rate to be
720%47 and besides, GDP per capita growth was one of the lowest in Latin America48. From
100 USD exported, 95 come from the oil exports49, which are carried out either directly by
PDVSA or by a multinational foreign company under the scheme of a joint venture with
PDVSA.
After Hugo Chavez, leader of what he called the Bolivarian Revolution (named after Simón
Bolivar, the national hero of the Independence War), took power of Venezuelan presidency in
1999, many changes were made in the oil industry. Just three years later, a coup d’etat was
organized and it was highly supported by the staff and an important quantity of workers of
PDVSA; due to the failure of this attempt, a serious purge was initiated and several highly
and extremely competent employees were fired. As a consequence, the company was
managed by bureaucrats who followed strong political and ideological guidelines.
During the presidency of the populist Hugo Chavez (1999-2012), oil prices reached high price
records; this expanded massively the national budget, creating an illusion of richness and
allowing the government to sell to the public the idea of strong and “crisis-proof“ budget
management. The injection of “petrodollars” boosted dramatically internal consumption and
luxury expenditure on foreign goods; and not just there wasn’t any significant long run
investment in the oil industry, but the foreign debt associated with the country and with
PDVSA increased. Nowadays, Venezuela is even more dependent on oil than ever.
The arrival to the presidency in 2012 of Hugo Chavez’s former Ministry of Foreign Affairs,
Nicolas Maduro, could have been an opportunity to correct the company’s strategies and
implement the structural reforms PDVSA deeply needs. However, to this day none of this has
happened, due to an apparently non-existing political will and to the difficulties to build a
national political consensus on how to carry out these changes.
The structural reforms that urgently need to be made
Despite the prestige and potential of the company, 2016 year found PDVSA in a very bad
financial shape, mainly due to the ideological orientation on its management during the last 15
years; therefore several structural changes need urgently to be done.
46
Venezuela Economic Outlook http://www.focus-economics.com/countries/venezuela
“IMF sees Venezuela inflation rocketing to 720 percent in 2016” By David Biller. Bloomberg. Pubilshed the 22
of January/2016. Taken 09 of May/2016. http://www.bloomberg.com/news/articles/2016-01-22/imf-seesvenezuela-inflation-rocketing-to-720-percent-in-2016
48
Ibídem.
49
Ibídem
47
15
First, it is necessary to free the company from its ideological and bureaucratic cage where it
has been for many years. Agreements on energy were made by the government with other
countries mainly for geopolitical and diplomatic reasons rather than financially orientations;
with the oil price decline the need for Venezuelan cheap oil has plumped down and these
countries have found themselves in a more comfortable situation to negotiate. Probably the
most important agreement of this kind in terms of oil quantity sells is Petrocaribe, which was
design to provide cheap Venezuelan oil to seventeen Caribbean countries50 in an international
context of very expensive oil (more than 100 USD per barrel). The payment conditions
include agreements such as deadlines from 2 years up to 25 years with an interest rate of 2%
(if barrel costs less than 40 USD) or 1% (if barrel costs more than 40 USD) 51. According to
PDVSA’s analysis, Venezuela holds about one third of the Caribbean’s external debt52, but on
the contrary of thinking that thanks to this Venezuela maintains a financial pressure on this
countries, reality shows it might be the opposite: on January of 2015, Dominican Republic
struck a deal to pay just $1,9 billion dollars for nearly $4,1 billions the country owned to
PDVSA. Jamaica seems to be following Dominican Republican example, and started
conversations to pay just $1,8 Billion for a total debt of $3,8 53. The reformulation of these
agreements doesn’t imply that pacts such as Petrocaribe must be ended, but to be reformulated
in order to put PDVSA into a better position without sacrificing economic and politic
resources.
Second, is to increase the production. Unfortunately there is not a clear source for statistical
data, however is admitted that there is a downward trend in oil production54. From 2008 there
has been a decline of 350,000 barrels per day and of more than 800,000 barrels per day since
its peak level in 1998. Although new reservoirs have been discovered (putting Venezuelan
reserves in the first place of the world) and despite oil prices skyrocketed for many years,
current production is estimated in 2.5/2.6 MM b/d, a lower level than a decade ago, as it is
shown by the graph. The western and north-east regions of the country produce conventional
50
Antigua and Barbuda, Bahamas, Belice, Cuba, Dominica, Granada, Guatemala, Guyana, Haití, Honduras,
Jamaica, Nicaragua, Dominican Republic, San Cristobal and Nieves, San Vicente and the Granadas, Saint Lucia,
Surinam and Venezuela
51
Petrocaribe: A handout, not a hand-up, which may soon run out. Presentation by RBC Caribbean at the 5 th
Biennial International Business, Banking and Finance Conference, UWI St. Augustine, Trinidad and Tobago.
May 2013.
https://sta.uwi.edu/conferences/13/finance/documents/Marla%20Dukharan%20PetroCaribe%20May%202013.pd
f
52
Ibídem
53
Jamaica Seeks Agreement to Pay Off Venezuelan Oil Debt. By Ezra Fieser. Bloomberg. Published on March
12, 2015. http://www.bloomberg.com/news/articles/2015-03-11/jamaica-seeks-deal-to-pay-off-venezuelan-oildebt-minister-says
54
“The impact of the decline in oil prices on the economics, politics and oil industry of Venezuela”. By
Francisco Monaldi. September, 2015. Center on Global Energy Policy. University of Columbia. Page 10
16
oil, whose production level has been decreasing. The Maracaibo Lake is one of the main
conventional oil reservoirs in the region, and its production has fallen from 1,1 millions of
barrels per day in 2008 to 750,000 barrels in 201455. The extra heavy production is increasing,
especially in the Orinoco Oil Belt, but due to the characteristics of this oil (less than 10°API),
liquid oil is required in order to bend it with it and create a commercial combination. Last
year Venezuela imported oil for the first time in its history, causing an enormous shock in the
public opinion and putting in doubt PDVSA’s capabilities to produce light oil. Another
problem to take into consideration is the cost of producing extra heavy oils, since it requires
the use of expensive techniques and equipment, not just to extract it but to transport it, storage
it and commercialize it. With current oil prices and a generally accepted forecast that –unless
something extraordinary such as a war in the Middle East happens- the oil barrel price will
not exceed 60 USD per barrel, it can be assume that extra heavy oil production will not be
increase in the short run.
Graph 3
Source: “The impact of the decline in oil prices on the economics, politics and oil industry of
Venezuela”. By Francisco Monaldi. September, 2015. Center on Global Energy Policy.
University of Columbia.
Third, the national market must be restructured. The demand for oil derivatives reaches
700,000 barrels per day56 and it represents a loss for PDVSA due to the obligation of
subsidizing it. The impact in terms of cost of opportunity was about $24 Billion USD in
55
56
Ibídem. Page. 11
Ibídem.
17
201357. If taken into consideration not just the national subsides but the international subsides
such as Petrocaribe or Cuba’s several commercial agreements, during 2013-2014 period
PDVSA got a cash flow from only about 1.4-1.5 million barrels per day58.
In brief, despite the enormous oil resources in Venezuela, its main oil company –PDVSAfaces important problems whose solutions implies to do urgent reforms. Three of them were
presented as the most important: to evaluate international agreements, which were formulated
more by political reasons than economical; to increase production in both extra heavy and
light oil; and to restructure national market, due to the colossal cost on subsidies. In order to
carry on these reforms, a first step must be done and this is to stop ideological pressure on
corporative management.
1.5 CANADIAN OIL SANDS
Alberta’s Oil sands are the third largest proven reserves of oil in the world after those located
in Saudi Arabia and Venezuela. The government of Alberta province, in Canada, defines oil
sands as “a naturally occurring mixture of sand, clay or other minerals, water and bitumen,
which is a heavy and extremely viscous oil that must be treated before it can be used by
refineries to produce usable fuels such as gasoline and diesel. Bitumen is so viscous that at
room temperature it acts much like cold molasses. New technologies are increasing the
treatment methods available to oil sands producers as more research is completed”59.
Currently, there are 315 billion of potentially recoverable oil in the oil sands. In order to boost
production and make the country a leader in the region (despite the US is rapidly increasing
its production rates too60) a combination of a more favorable economic frame and new
technologies of extraction and processing are required61.
Two methods are used to extract oil sands in Alberta: in situ and mining. For each one of
them, there are different specific procedures62:
1. In Situ
a. Primary recovery
57
Ibídem
Ibídem.
59
Alberta Energy website. Taken the 10 of May, 2017. http://www.energy.alberta.ca/OilSands/793.asp
60
Ratner, Michael and Tiemann, Mary “An overview of Unconventional Oil and Natural Gas: Resources and
Federal Actions”. Congressional Research Center. April 22, 2015.
61
Íbidem
62
Canadian Oil Sands Supply Costs and Development Projects (2016-2026). Study No. 163. February 2017.
Canadian Energy Research Institute. Page 7.
58
18
b. Thermal recovery
c. Solvent-based recovery
d. Hybrid thermal/solvent processes.
2. Surface Mining
a. Stand-alone mine
b. Integrated with an upgrader
Energy consumed by Oil Sands Industry
Next diagram shows the cycle of oil sands energy requirements, sources and outputs. Oil
sands projects, which are energy-intensive in operations, can be divided into four categories:
thermal energy demand, electricity demand, Hydrogen demand for upgrading and demand for
transportation fuels (like diesel fuel) (CERI, 2017).
Diagram 1
Source: CERI, 2017. Study No. 163 and author’s own calculations
Under this framework, is clear that Oil Sands demand for energy is affected by certain
variables, which behavior might have considerable impacts on energy costs and therefore
investment revenues. Diagram 2 shows a list of factors that affect OS demand for energy,
possible energy supply sources and GHG emissions.
19
Diagram 2
Source: CERI, 2017. Study No. 163 and author’s own calculations
Change in oil sands demand for energy is affected by a set of uncertain variables that include
overall industry production volumes and project-specific energy intensities (CERI, 2017).
Increases in Oil Sand (OS) production, decline of existing projects due to time and
technological innovations and productivity directly affect and speed up the amount of energy
consumed by oil sands industry.
1.6 THE RUSSIAN CONTRIBUTION
Russia owns big reserves of heavy oil. OJSC “Tyumenneflegaz”, a subsidiary of Rosneft have
been setting up pilot works in Russkoe oil and gas field, discovered in 1968. Located beyond
the Polar Circle, its development must deal with a number of technical challenges: high crude
viscosity, remote location, considerable heterogeneity, compartmentalization of poorly
cemented sandstones, presence of extensive gas cap, bottom water and thick permafrost zone.
20
During 2009-2012, 23 wells (16 of them horizontal) were drilled in pilot areas, injecting cold
and hot water to find an effective system for full-field development63.
The complexity of this kind of oil is probably the biggest threat to any investment project.
The geology of many of the reservoirs is not homogeneous, costs are large, decline production
rates are rapid; besides, Russia’s taxes are high and tax system is not completely appropriate
for these investments (it is based more on revenues rather than profits)64.
Yaregskoye field is also one of the most important high-viscosity oil field in Russia. It was
discovered in 1932 and is located in the south of Timan-Pechora Province, Ukhta District of
Komi Republic. It covers two major areas under development: Yaregskaya and Lyaelskaya.
Currently is being developed by Lukoil. To 2015 it had 321 million barrels of oil proved
reserves and a production of 740 thousands of tons65.
Significant reserves of heavy and extra viscous bituminous oil exist in the Republic of
Tatarstan, Russia. The company PJCS Tatneft indicates that 450 deposits have been
identified, containing over 1,4 Billion tonnes confined with a depth of 50-250 meters. This oil
is being extracted through horizontal drift wells which produced up to 20 tons per day, 8-10
times higher than if vertical wells were used66
The Russian Ministry of Natural Resources has targeted a goal of 1 mmbpd of shale oil
production by 2025, an extremely ambitious objective. In order to do so, Russian companies
are creating ventures with International Oil Companies to exploit these resources in several
fields. The oil service industry must fulfil the demand for new equipment and technology;
specifically it must be able to produce a bigger number of heavy oil rigs capable of drilling
deep horizontal wells, which might cost up to $15 Billion of dollars67.
63
Technical Paper: Russkoe High Viscous Oil Field – Production and Performance Optimization. SPE Society.
Paper Number: 170086. 2014. http://www.slb.com/resources/technical_papers/sand_control/170086.aspx
64
Henderson, James. Tigh Oil Developments in Russia. WPM 52. October, 2013. The Oxford Institute for Energy
Studies. Page 1-2
65
“The Yaregskoye Field”. Taken 15 of May/2017.
http://www.lukoil.com/Business/Upstream/KeyProjects/TheYaregskoyefield
66
“Development of Natural Bitumen Fields”. Tatneft coporative website. Taken on May 15, 2017.
http://www.tatneft.ru/production-activity/technologies/development-of-natural-bitumen-fields/?lang=en
67
Íbidem. Page 21
21
CHAPTER II. METHODOLOGICAL FRAMEWORK
2.1 OIL MARKETS AND NATIONAL ECONOMIES
The relation among oil prices and macroeconomic stability has been considered by numerous
researches. One of the most important topics to study is the role of monetary policy for
preventing negative consequences of external oil price shocks. Bernanke, Gertler and Watson
(1997) suggest that monetary policy can be used to neutralize recessionary consequences of
an oil price shocks. Hamilton and Herrera (2004) challenged that hypothesis based on two
arguments: first, Federal Reserve might not have the power to implement such a policy and
second, the size of the effect of oil shocks is considerable smaller than analyzed by other
researches, due to the use of a different methodology. In a response, Bernanke, Gertler and
Watson (2004) stated: “Using a modified VAR framework, we considered counterfactual
scenarios in which mone- tary policy (represented by the level of the federal funds rate) does
not respond to an oil pnce shock. We found that the adverse effects of an oil pnce shock on
output are reduced considerably when the endogenous response of the funds rate is "shut off."
Indeed, our point estimates suggested that the endogenous response of monetary policy
accounted for virtually all the negative impact of the oil shock onoutput (though, as we
discuss in the paper, there is considerable sampling uncertainty about the true response)”.
Marion and Svensson (1984) consider a situation where a small economy is adjusted with
expected and unexpected oil price increases. Authors calculate changes in output, oil imports,
consumption, investment, welfare, the trade balance and the current account of a small oilimporting economy. Elder and Serletis (2010) expressed that oil prices might affect economic
activity through different channels; therefore they use an empirical model that estimates all
the parameters of interest, based on a structural-modified VAR.
Although calculating cost is already a demanding task, calculating oil prices in international
markets in order to forecast revenues is not a less challenging job. In a Working Paper Series
for the European Central Bank, Manescu and Van Robays (2014) demonstrated “how the
real-time forecasting accuracy of different Brent oil price forecast models changes over time”
(Manescu, Robays, 2014. pp 1). In their opinion, the instability in the performance of all
models was hiding important information which was corrected by them proposing a fourmodel combination (consisting of futures, risk-adjusted futures, a Bayesian VAR and a
Dynamic General Stochastic Equilibrium –DGSE- model of the oil market).
22
About the oil price, there are many papers analyzing its behavior, however one of them was
taken into special consideration. In the already mentioned paper written by Manescu and
Robays (2014), from the European Central Bank, a forecast was presented for Brent oil price
proposing a forecast combination of futures, risk-adjusted futures, a Bayesian VAR and a
DGSE model of the oil market. Their conclusions are quite remarkable and can improve the
analysis of Oil income done in this paper. In a more specific aspect, Chen, Deng, Huand and
Qin (2015) specifically analyze the impact of fluctuating prices in an oil refinery, applying
dynamic programming methods to solve a multi-period multiproduct optimization problem.
“We use GBM to model oil price behaviors and build a multi- period stochastic dynamic
programming model to optimize refinery operations. The model structure implies that
refineries can separate decisions into long-term inventory planning decisions and short-term
operation decisions. We use OA sampling and MARS algorithm to solve the multiperiod
model approximately. We solve the multiperiod model numerically based on refinery data
from a refinery in China and obtain the optimal solution of the approximate problem.
Numerical results show that the OA/MARS algorithm performs well in computing time and
accuracy.68” (Huand and Qin, 2015, page 7).
2.2 ANALYSIS OF APPROACHES OF INVESTMENT EVALUATION IN OIL
INDUSTRY
Several authors have considered the topic of analyzing investments in energy sectors,
specifically in oil industry. However, to this day there is not still a total agreement about
which model is the most accurate. One of the most important problems these investment
models face relies on the incapacity of properly forecasting and then managing the risk and
the uncertainty of this sector, especially when considering the stochastic dynamic of oil prices
and costs.
The most important question of investment in oil and gas projects is which method to use. Lin
and Ji (2007) perform a very interesting comparison between real option methods and
financial options, and determined that the first is more suitable for oil/gas project, then they
built up a new model extended from the Black-Scholes and tested it within a computational
case, “Traditional methods based upon discounted cash flow (DCF) reported in the finance
literature are always based upon static assumptions – no mention about the value of
68
OA means orthogonal array sampling method; MARS means multivariate adaptive regression splines and
GBM means geometric Brownian motion processes.
23
embodied managerial options” (Lin and Ji, 2007. page 2). Moreover, they identify risk
managing as a key factor to take investment decisions “The valuation of projects and business
opportunities using DCF and real option theory (ROT) is based on cost-benefit analysis, but
they are different in the treatment of risk. Risk is the possibility of loss or gains since future
values are dispersed around expected mean values so that the only way to measure risk is
using probabilistic approach. Mathematical models for option valuation were firstly
developed to price those on common stocks, exchange and interest rates, among others and
later migrated to value real options on real assets from industrial projects. Volatility is not
preset in DCF, but plays a remarkable role in option pricing” (Lin and Ji, 2007. Page 2).
Some years before, Cortazar, Schwarts and Casassus (2001) also used a real options model
under price and geological-technical uncertainty which was successfully applied to a copper
exploration prospect. Their findings were useful because they highlight the option of stopping
the exploration investment schedule “the model considers that the exploration investment
schedule may be stopped and/or resumed at any moment depending on cash flows
expectations, which depend on current commodity price and geological-technical
expectations. Once all exploration phases are concluded the project is modelled as having the
flexibility of postposing development investments and, once developed, as having the option to
close or reopen production” (Cortazar, Schwartsa and Casassus, 2001, page 8). Under a
similar objective of deciding the best method for optimizing investments, Midthun, Fodstad
and Hellemo (2015) mentioned that Combinatorial optimization models might help decisionmakers to take better decisions on which elements to invest in, at what time and with what
capacity, they also presented a optimization model for analysis of system development for
natural gas fields, processing and transport infrastructure. Qin, Wang and Xue (2015) built a
multi-factor analysis with a real options model for investment in deep-water oil and gas
exploration projects, addressing the importance of oil price, geology and engineering
uncertainties. As for the evaluation of deep-water oil and gas projects, they used a numerical
analytical method and “the evaluation result shows that the multi-factor real options model
could be more accurate than the single-factor model. This multi-factor model gives investors
more reliable theoretical supports to make reasonable decisions. Our sample project has
been operated for more than five years, and the real practice has also showed that the
estimated value with multi-factor real options model is a better approximation to reality. So
the multi-factor real options model could be a good reference approach for investment
decisions about deepwater oil and gas projects”. (Qin, Wang and Xue, 2015, page 9).
Baoshend and Qing (2011) preferred to use system dynamics to analyze and forecast
investment scale and structure in upstream sector for oil companies and found that: “The
24
results indicate that the upstream investment of the oil company is significantly effected by
international oil price, expected oil and gas reserves and expected oil and gas yield. The oil
company should take some active and reasonable measures to cope with the influences of
various factors and make some adjustments on the scale and structure of investment.”
(Baoshend and Qing, 2011, page 6). On another aspect, Ettehadtavakkol, Jablonowski and
Lake (2016) compared Monte Carlo (MC) simulation and stochastic programming for
development optimization and uncertainty analysis; they concluded that while MC method
generates more useful information, stochastic programming method is more computationally
efficient in determining the optimal solution.
Gaspar, Barreto and Schiozer (2016), presented and optimization design for oil exploitation
strategy, in which they use the Producer Well Economic Indicator (PWEI) as a valid indicator
for scheduling a well opening -it was first developed by Ravagnani et al. (2011) -. Branka,
Zdravka and Tea (2014) presented a brief review of Binomial and Trinomial models for
Valuation Options and of Finite Difference Method in Option Pricing. Although their work is
focused on a computational framework, it is important to consider it in the sense that it gives
tools to understand better oil prices behavior, which are based on derivatives traded using
both: American and European methods.
Zhong and Zhao (2016) showed a model for optimal oilfield development investment, which
included only two types of investment: exploration and development: “With respect to the
investment structure in exploration and development of oil companies, the following ideas are
drawn: the relation between proven reserves and exploration investment can be described by
exponent model; the relation model between oil productivity and oil development engineering
construction investment can be got with DEA method; the optimal solution of long-term total
NPV model can be got with genetic algorithms and simple programming. Above methods can
provide foundation for petroleum companies to make longterm exploration and development
strategic planning. Moreover, they may also provide references for reducing blindness and
subjectivity during exploration and development investment.” (Zhong and Zhao, 2016, page
5-6). In this paper, the author considers one more type: investment in transportation.
Probably one of the most important papers written on this topic, was done by Brenna and
Schwarts (1985), showing that techniques of continuous time arbitrage and stochastic control
theory may be used to value natural resource investments, as long as optimal policies for
developing, managing and abandoning them. They argue that traditional standard techniques
of evaluation take expected cash flows from an investment model and discount them at a rate
which takes into consideration the risk; later, this result is compared with the cost of the
25
project. When output prices vary above a specific rate –due to inevitable market dynamic-, the
model stops being accurate and fail in prevailing risk and uncertainty. As a result, authors
built a model where output prices are assuming to be stochastic and where managerial
decisions of reducing, increasing or even shutting down the production might be taken. In
addition, according to authors Smith and McCardle (Smith and McCardle, 1999), almost
every capital expenditure firm evaluates its investments options using sensitive analysis to
identify risks and uncertainties, before developing models to calculate expected Net Presented
Values (NPV) and distributions on NPVs. Although this approach is widely used, it lacks of
two advantages. First, cash flows and their resulting decisions are not steady but dynamical;
for example, a company might change its production level requirements without taking into
consideration previous decisions. As a consequence, the designed model is less accurate.
Secondly, discount rate (calculated as risk-adjusted discount rate to include uncertainty) could
undervalue projects with long time horizons (a common time horizon is 30-40 years) as a
result of setting the rate above the rate for risk-free borrowing and lending. In order to
improve this methodology, authors described some lessons learned by the “Valuation
Methods Improvement (VMI)”, which was an interdisciplinary team with the task of reach a
better understanding of new approaches.
Another approach is presented by Constance Helftat (Helftat, 1989). She develops a model
where firms take into consideration not a particular investment return but the covariance of
returns of several investments done by the firm. A portfolio model is used to describe how an
average petroleum firm reduces its risk minimizing the variance of the forecast present value
per dollar over all projects (ex-ante discounted cash flows divided by initial fixed costs),
subject to a required expected rate of return constraint on the portfolio.
Graca Ermida analyzed the situation of non-conventional oil in the Arctic and concluded:
“The Arctic will likely continue to be on the radar for most global oil and gas companies.
However, the technical challenges and high costs of exploring the region will remain an
important factor in the decision making process of major IOCs. In order for companies to be
able to cope with the level of investments and challenges in the Arctic, their strategies should
include approaching the region in a more comprehensive way. In most cases, oil and gas
companies already partner among themselves to explore the area, to address these
challenges. The success of these ventures is highly contingent on Arctic countries long-term
policies”. (Ermida, 2013, page 7).
Several documents were taken into consideration to specifically analyze heavy oil shields.
Canadian information was taken mainly from documents published by Canadian Energy
26
Research Institute, Alberta Oil Sands Industry –Quarterly Update, published by the Alberta
Government and Suncor Energy INC. annual report 2015. Venezuelan information was taken
mainly from the official website of PDVSA and Center on Global Energy Policy from the
University of Columbia. The Oxford Institute for Energy Studies offered pertinent
information about Russia’s shale oil, as well as Russian companies such as Lukhoil and
Tatneft. Furthermore, Congressional Research Service of the United States offered an
excellent description of the advances done by that country in non-conventional oil production.
World Energy Outlooks from International Energy Agency (IEA) offered remarkable
information about global energy markets and trends. The objective of its projections is not to
exactly forecast what will happen, but to propose what might happen given specific
assumptions and methodologies. In the Reference case scenario, for example, a business-asusual projection was done with the aim of estimating trends of future global energy
consumption and production. For this, an energy model was built and demographical,
economic and technological variables, among others, were included.
Any model is a simplified representation of bigger situation. Therefore, energy models
pretend to illustrate the global situation of energy markets, which are characterized by a high
complexity and dynamism. Moreover, there are non-economic variables that affect the
economy and their effects are hard to identify and valuate. Examples of these kinds of
variables are international agreements about climate change that might reduce energy
consumption, changes in habits –going to work in bike rather than in car-, natural catastrophes
(tsunamis, hurricanes, earthquakes), improvement of technology applied to renewal and nonrenewal energy sources, international politics like sanctions to oil producers (Iran and Russia,
for example) or consumers (OPEC embargo to the US in the seventies) or even a war. It’s
impossible to predict with exactitude all of these events, so scenarios are built to take into
account these possible situations. Logically, sometimes estimations might not be extremely
precise in values or dates, but it gives a powerful idea about the markets and where are they
going.
27
CHAPTER 3. INVESTMENT MODEL
3.1 IDENTIFYING THE VARIABLES
1. Object of Analysis
This document will take Canadian oil sands, located in the Province of Alberta, as the object
of analysis. All the calculations and simulations will be done for this region already described
in Chapter 1. The reason why this specific field was chosen relies on the big amount of
credible and updated official information available online.
2. Investment
The amount of investment was analyzed by Canadian Energy Research Institute (CERI)
following internal calculations which equations and formulas were not published69. The
author of this document did not apply any discount rate to investment’s values, considering a
discount rate was already applied to those amounts by CERI.
A. Total Capital Costs Forecast (Million CDN$), Reference Case Scenario, incl. Initial
and Sustaining Capital
a. Total In situ Thermal & Solvent
b. Total Primary and EOR projects
c. Total Mining
d. Total Upgrading
B. Total Operating Costs Forecast (Million CDN$), Reference Case Scenario
a. Total In situ Thermal & Solvent
b. Total Primary and EOR projects
c. Total Mining
d. Total Upgrading
69
Ibidem
28
Graph 4
Investment (Billions of USD)
55 USD
50 USD
45 USD
40 USD
35 USD
30 USD
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
25 USD
Investment (Billions of USD)
Source: CERI, 2017. Study No. 163 and author’s own calculations
3. Oíl Price Dynamic
Forecasting oil prices with accuracy for next decades would exceed the reach of this paper
and is not the goal of this document; and even if this is tried, oil prices do not behave just as
economic variables, but are also affected by unpredictable non-economic shocks. With this
problem in mind, it was preferred to apply three scenarios for oil price (Brent benchmark)
from 2016 until 2040, as it was developed by the U.S. Energy Information Administration on
its International Energy Outlook 2016.

High Oil Prices case. The price for Brent rises to $252 USD/barrel in 2040.

IEO2016 Reference case. The price for Brent rises to $141 USD/barrel in 2040.

Low Oil Prices case. The price for Brent rises to $76 USD/barrel in 2040.
An exponential formula was built with the aim of knowing the average annual growth rate
from 2016 with a 50 USD/barrel price.
𝑛
𝐹
√ −1=𝑔
𝑃
Formula 1
Where:




F = Future price of oil (2040)
P = Present price of oil (2016)
n = amount of time (24 years)
g = average annual growth rate of oil production
29
Table 1
Average annual growth rate of oil price for 24 years (2016-2040)
High Oil Price case
7%
IEO2016 Reference case
4%
Low Oil Price Case
2%
Source: CERI, 2017. Study No. 163 and author’s own calculations
4. Exchange Rate Dynamic
The investment amounts were expressed in Canadian Dollars (CAD), while the rest of the
data considered United States Dollars (USD), in order to unify the expressions the historical
exchange rate was taken from 16/05/2007 to 17/04/2017. During this ten years period the
exchange rate has oscillated among 0,9 USD/CAD and 1,5 USD/CAD, with a mean of 1,1091
USD/CAD and a standard deviation of 0,1258. For this document’s purposes, a constant
exchange rate of 1,2 USD/CAD was taken.
Graph 5
USD/CAD
1.5 CAD
1.4 CAD
1.3 CAD
1.2 CAD
1.1 CAD
1.0 CAD
0.9 CAD
0.8 CAD
USD/CAD
Source: Author’s calculations and Canadian Forex70
5. Choosing the right Benchmark
The U.S. Energy Information Administration (EIA) on its International Energy Outlook 2016
forecast three possible trends for future oil prices. However, these prices were expressed using
the Brent benchmark, while the analysis is made using West Texas Intermediate benchmark.
70
Data taken from 16/05/2007 to 17/04/2017 from the site: http://www.canadianforex.ca/forextools/historical-rate-tools/historical-exchange-rates
30
Therefore it was necessary to convert from one benchmark to another. The European BRENT
Spot Price FOB was taken in dollars for the period: May 20, 1987 – April 10, 2017.
Graph 6
Europe Brent Spot Price FOB (Dollars per Barrel)
150 USD
100 USD
50 USD
0 USD
Europe Brent Spot Price FOB (Dollars per Barrel)
Source: Author’s calculations and EIA Databases
The WTI Spot Price FOB was taken in dollars for the period: January 02, 1986 – April 10,
2017.
Graph 7
WTI Spot Price FOB (Dollars per Barrel)
160 USD
140 USD
120 USD
100 USD
80 USD
60 USD
40 USD
20 USD
0 USD
WTI Spot Price FOB (Dollars per Barrel)
Source: Author’s calculations and EIA Databases
The historical price was taken from January 08, 2010 to April 10, 2017 for both Brent and
WTI benchmarks, then the spread was calculated (Brent-WTI).
31
Graph 8
Spread Brent-WTI
40 USD
30 USD
20 USD
10 USD
0 USD
2010
2011
2012
2013
2014
2015
2016
2017
-10 USD
-20 USD
Spread Brent-WTI
Source: Author’s calculations and EIA Databases
During the most part of the analyzed time, BRENT price was higher than WTI price. The
mean of the spread for all this period is 7,31 USD. Therefore, for this paper’s purposes, the
WTI prices were calculated as 92% of the BRENT prices for each of the three price scenarios.
In other words, WTI price is the BRENT price less 8% (lightly higher than the historical
average).
6. Oil Production Dynamic
The Canadian Energy Research Institute (CERI) on its newest Canadian Oil Sands Supply
Costs and Development Projects (2016-2036) published on February 2017, forecasted three
scenarios for oil sands production:

High Case Scenario: 6,6 millions of barrels per day by 2036.

Reference Case Scenario: 5,5 millions of barrels per day by 2036.

Low Case Scenario: 4,5 millions of barrels per day by 2036.
For each of these scenarios, CERI created a model with a specific amount of oil produced for
each year from 2016 until 2036 and the specific amount of money planned to be invested on
oil sands production for the same period of time (in Canadian Dollars). Following graphs
show the behavior of oil production growth for the analyzed period.
Taking into account there are three scenarios for prices and three scenarios for production, in
total nine scenarios are assumed:
32
Table 2
Oil Prices
Scenarios
1. High Oil Price Case
High Case
2. IEO2016 Price Case
3. Low Oil Price Case
Quantity of
Production
1. High Oil Price Case
Reference Case
2. IEO2016 Price Case
3. Low Oil Price Case
1. High Oil Price Case
Low Case
2. IEO2016 Price Case
3. Low Oil Price Case
This document will based its calculations on the Reference case for Production and IEO2016
for oil price.
Graph 9
Average annual growth of production
(Millions of Barrels/day)
7
6
5
4
3
2
High Case Scenario
Reference Case Scenario
Low Case Scenario
Source: CERI, 2017. Study No. 163 and author’s own calculations
Reference Case Scenario:
33
Graph 10
Quantity Barrels per Day (Millions)
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
6.00
5.50
5.00
4.50
4.00
3.50
3.00
2.50
2.00
Quantity Bbl Day
Source: CERI, 2017. Study No. 163 and author’s own calculations
Graph 11
Quantity Barrels per Year (Millions)
2,500.00
2,000.00
1,500.00
1,000.00
500.00
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
-
Quantity Barrels per Year (Millions)
Source: CERI, 2017. Study No. 163 and author’s own calculations
A standard exponential formula was used to identify the rate for the increase of production:
𝐹𝑢𝑡𝑢𝑟𝑒 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 = 𝑃𝑟𝑒𝑠𝑒𝑛𝑡 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 ∗ (1 + 𝑋 )𝑛
Then:
Formula 2
34
𝑛
𝐹
√ −1=𝑋
𝑃
Formula 3
Where:
1. n, equals 24, considering the starting year is 2012 and the ending year is 2036
2. P, means starting Production (2012) is 1.135.373,11 barrels of oil.
3. F, means Future production changes depending on the scenario:
a. High Case Scenario: 6.395.506,24 Barrels of oil
b. Reference Case Scenario: 5.274.860,53 Barrels of oil
c. Low Case Scenario: 4.347.683,42 Barrels of oil
4. X, represents the rate of increase in production
Under these variables, results for increase rate for the three scenarios are:
1. High Case Scenario. Increase rate: 5,573%
2. Reference Case Scenario. Increase rate: 4,729%
3. Low Case Scenario. Increase rate: 3,889%
35
3.2 BUILDING THE MODEL
1. Increase in Production
To the total production dynamic already specified, a technology rate was added in order to
consider the importance of productivity in the model and its effect on total income.
Technology variable starts at year 2016 and finishes in 2036, this procedure was done with
the following formula:
Total Income = ∑20
𝑇= 0
𝛽 𝛾
𝐴𝛼
𝑇 𝑃𝑇 𝑄𝑇
(1+𝛿)𝑛
Formula 4
Where:
1. Starts from 0 (zero) and goes to 20 (which means, starts from 2016 and goes to 2036)
2. A, Technology
3. P, the price of oil
4. Q, quantity of oil produced and sold (storage is not considered)
5. n, period of time (20 years, from 2016 to 2036)
6. α, increase in technology
7. β, increase in price
8. γ, increase in quantity produced and sold
9. δ, discount rate
2. Specifying the methodology
Taking into account that there are 3 price scenarios set up by IEO (2016) and 3 production
scenarios considered by CERI, a simplified Net Present Value (NPV) investment model was
built. A NPV model was chosen over a Real Options approach considering that in this paper
neither any managerial intuitive decisions nor non-economical strategic thinking is allowed to
be considered. Real Options has the advantage of allowing managerial flexibility when taking
investment decisions, as well as adjusting the model to new variables that are not considering
in NPV (such as the possibility of obtaining market power, entering into a new market, or
weaken competition, even if these give short-term negative revenues as a result). Although
this approach seems to be more feasible for oil investments, it is not consider in this document
and therefore opens opportunities for further research.
36
To the NPV model was added a variable describing the effect of technology on the increase of
production, which therefore affects the revenues. This variable (A), starts at 1 and grows at an
exponential rate “α=1%” assumed by the author.
𝐴𝛼 𝑃 𝑄
−𝐼
𝑡
𝑡 𝑡 𝑡
20
𝑁𝑃𝑉 = ∑20
𝑡=0 (1+𝛿)𝑡 + ∑𝑡=0 (1+𝛿)𝑡
Formula 5
Although NPV is frequently used when taking oil investing decisions, it is not accurate
because it is not a dynamic model able to properly manage the fluctuation and volatility of the
variables (oil price, quantity of oil sold, quantity of oil produced, etc.). In order to solve this
problem, as already explained, two important variables (oil price and quantity produced) were
divided under three categories with different minimum and maximum values, rather than
forecasting their exact daily, weekly or monthly value for next 20 years.
This model doesn’t take specifically into consideration one important variable: cost.
Therefore, a new model was built in order to offer more accurate results:
∑20
𝑇=0
𝛽 𝛾
T
𝐴𝛼
𝑇 𝑃𝑇 𝑄𝑇 −𝐼𝑇 −Ɵ𝑇 −λ𝑇 − 𝑅𝑂𝑌𝑇 −𝜏𝑇 − 𝜋𝑇 − 𝜌𝑇 −𝑇𝑇
(1+𝛿)𝑇
Formula 6
Where:
𝛽
𝛾
1. 𝐴𝛼𝑇 𝑃𝑇 𝑄𝑇 , Represents the total amount of oil produced and sold.
2. 𝐼𝑇T . Represents the total amount of investment (Total Operating Costs and Total Capital
Costs)
3. Ɵ𝑇 ,Represents Operating Working Capital
4. λ 𝑇 , Represents Fuel (Natural Gas)
5. 𝑅𝑂𝑌𝑇 , Represents Royalties.
6. 𝜏 𝑇 , Represents Income Taxes
7. 𝜋𝑇 , Represents Emissions Compliance Costs
8. 𝜌𝑇 , Represents Abandonment Costs
9. 𝑇𝑇 , Represents Transportation Costs
10. 𝛿, Sigma, represents discount rate
It is important to mention that these new costs are not represented in the variable for
Investments (I), since it only considers capital and operational costs.
3. Income and Revenues
Assuming Technology increases at an annual rate of 1%, the following charts show how
much yearly income and yearly revenues change through time. It is worth seen the influence
37
of technology in the variables’ behavior in the long run, creating a spread with the variables
without technology for both, income and revenues.
Graph 12
Total Income (Billions of USD) for
Technology ratio: 1%
95 USD
85 USD
75 USD
65 USD
55 USD
45 USD
Total Income with Technology
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
35 USD
Total Income without Technology
Source: CERI, 2017. Study No. 163 and author’s own calculations
Graph 13
Source: CERI, 2017. Study No. 163 and author’s own calculations
Under the described circumstances the revenues are negative until 2023, when they start to
growth with an exponential form. With the aim of determining the behavior of revenues for
different values of technology, the following analysis is done.
38
4. Technological Effect on Revenues
With the aim of measuring the effect of technological change and discount rate on total
revenues (caeteris paribus), a simulation was made for 11 values of Technology (from 0% to
10%) and 10 values of Discount Rate (from 0% to 9%).
Description of Variables:
1. Investment model used: Net Present Value
2. Starting year: 2016
3. Scenario assumed:
a. Oil Production: Reference Case
b. Oil Price: IEO2016 Oil Price Case
4. Period of time: 2016-2036, 20 years.
5. Exchange rate CAD/USD: 1,2 (fixed for the entire period)
6. Difference In prices of Benchmarks: WTI is always equivalent to 92% of Brent.
7. Annual increase in oil prices: 4%
8. Days in a year: 365
9. Rate of increase in technology: 1% annually
10. Annual Discount rate: 5% (real), assumed by the author
The technology rate α was changed for a yearly increase from 0% to 10%, and the Net Present
Value of Revenues for each change were analyzed; besides, the change of NPV expressed in
percentage was also taken into consideration.
It is important to mention that no specific costs for technology were assumed.
Table 3
Technology
NPV Revenues (Millions
Variation of NPV of
of USD)
Revenues in Percentage
0,0%
81.696,94 USD
1,0%
226.842,33 USD
177,7%
2,0%
392.687,57 USD
73,1%
3,0%
582.307,90 USD
48,3%
4,0%
799.230,92 USD
37,3%
5,0%
1.047.500,93 USD
31,1%
39
6,0%
1.331.751,88 USD
27,1%
7,0%
1.657.290,28 USD
24,4%
8,0%
2.030.189,03 USD
22,5%
9,0%
2.457.393,75 USD
21,0%
10,0%
2.946.843,14 USD
19,9%
Source: CERI, 2017. Study No. 163 and author’s own calculations. A discount rate of 5% was
considered.
Graph 14
Total Revenues due to a change in Technology
NPV Revenues (Millions of USD)
3,500 USD
3,000 USD
2,500 USD
2,000 USD
1,500 USD
NPV Revenues (Millions of
USD)
1,000 USD
500 USD
0 USD
Technology Annual Growth Rate
Source: CERI, 2017. Study No. 163 and author’s own calculations.
Change of NPV Revenues in percentage
Graph 15
Variation of Total Revenues due to a change in
Technology (Percentage)
200%
150%
100%
NPV of Revenues in
Percentage
50%
0%
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0%
Technology Annual Growth Rate
Source: CERI, 2017. Study No. 163 and author’s own calculations.
40
If technology annual growth changes from 1% to 2%, then total value of NPV revenues
(meaning, the sum for all the 20 years) will increase 73,1%. If it changes from 2% to 3%
(assuming a 5% discount rate), then total value of NPV revenues will increase 48,3%. Total
revenue is affected positively by an increase in technology, as expected. Higher technology
increases production growth which increases income and revenues. Theory of decreasing
marginal revenues explains why Total NPV increases every time in a smaller proportion as
Technology gets higher.
Table 4
Sigma
NPV Revenues (Millions of USD)
NPV of Revenues in Percentage
0,0%
317.326,33 USD
1,0%
250.809,36 USD
-21,0%
2,0%
195.868,44 USD
-21,9%
3,0%
150.438,48 USD
-23,2%
4,0%
112.838,47 USD
-25,0%
5,0%
81.696,94 USD
-27,6%
6,0%
55.892,50 USD
-31,6%
7,0%
34.506,21 USD
-38,3%
8,0%
16.783,48 USD
-51,4%
9,0%
2.103,40 USD
-87,5%
Source: CERI, 2017. Study No. 163 and author’s own calculations.
Graph 16
400 USD
300 USD
200 USD
NPV Revenues (Billions
of USD)
100 USD
0 USD
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
NPV Revenues
Total NPV Revenues (Billions of USD)
due to a change in Discount Rate
Sigma
Source: CERI, 2017. Study No. 163 and author’s own calculations.
41
Graph 17
9.0%
8.0%
7.0%
6.0%
5.0%
4.0%
3.0%
2.0%
1.0%
0%
-10%
-20%
-30%
-40%
-50%
-60%
-70%
-80%
-90%
-100%
0.0%
Change of NPV Revenues in percentage
NPV of Revenues in Percentage
NPV of Revenues in
Percentage
Sigma
Source: CERI, 2017. Study No. 163 and author’s own calculations.
An increase in discount rate affects negatively Total revenue. Considering discount rate is
dividing the revenues, a higher value of sigma increases the denominator and reduces the
value of NPV. Theory of increasing marginal revenues explains why NPV decreases more
with higher values of sigma. For example, if Sigma changes from 1% to 2%, Total NPV
Revenues will decrease in 21,9% (assuming there is no technological growth).
Despite discount rate also affects Investment (which affects negatively NPV as well), the
value for Income is higher than the value of investment, therefore the relation among sigma
and NPV is inversely proportional.
5. Effect of Technology in Costs
The proposed investment model considers eight different kinds of expenses and only one kind
of income: oil sales revenues (gas revenues are not taking into consideration). Reviewing the
annual report about oil sand costs, it was created a table of costs per barrel for oil sands in the
Alberta region under SAGD extraction method (from 2011 to 2015, excepting 2012 because it
was not presented in the reports).
Table 5
SAGD/bbl
Average
Proportion
Fixed Capital (Initial & Sustaining)
16,86 USD
24%
42
Operating Working Capital
0,38 USD
1%
Fuel (Natural Gas)
4,23 USD
6%
Other Operating Costs (incl. Elec.)
9,85 USD
14%
Royalties
7,46 USD
11%
Income Taxes
2,64 USD
4%
Emissions Compliance Costs
0,30 USD
0%
Abandonment Costs
0,03 USD
0%
TOTAL PRODUCTION COSTS
41,75 USD
59%
Transportation Costs
17,46 USD
25%
TOTAL COSTS
59,21 USD
83%
Source: CERI, 2017. Study No. 163 and author’s own calculations
Graph 18
Total Costs in average
Transportation
Costs
29%
Fixed Capital
(Initial &
Sustaining)
28%
Abandonment
Costs
0%
Emissions
Compliance
Costs
Income1%
Taxes
4%
Operating
Working Capital
1%
Royalties
13%
Fuel (Natural
Gas)
Other
7%
Operating Costs
(incl. Elec.)
17%
Source: CERI, 2017. Study No. 163 and author’s own calculations
An increase of one percent (1%) in the cost of each of the eight factors gives the following
results:
Table 6
Type of Cost
Variable
High Case Scenario
Reference Case
Low Case Scenario
Scenario
Fixed Capital
Without
(Initial &
Change
Sustaining)
With Change
2.052.412.059,33 USD
1.765.655.251,51 USD
1.521.533.084,14 USD
2.058.258.245,20 USD
1.770.684.625,96 USD
1.525.867.089,67 USD
43
Difference
Operating
Without
Working
Change
Capital
With Change
Difference
Fuel (Natural
Without
Gas)
Change
With Change
Difference
Other
Without
Operating
Change
Costs (incl.
With Change
Elec.)
Royalties
Difference
Without
0,2848%
0,2848%
0,2848%
2.052.412.059,33 USD
1.765.655.251,51 USD
1.521.533.084,14 USD
2.052.544.221,41 USD
1.765.768.948,30 USD
1.521.631.061,04 USD
0,0064%
0,0064%
0,0064%
2.052.412.059,33 USD
1.765.655.251,51 USD
1.521.533.084,14 USD
2.053.878.841,73 USD
1.766.917.099,50 USD
1.522.620.467,12 USD
0,0715%
0,0715%
0,0715%
2.052.412.059,33 USD
1.765.655.251,51 USD
1.521.533.084,14 USD
2.055.825.163,03 USD
1.768.591.486,68 USD
1.524.063.350,98 USD
0,1663%
0,1663%
0,1663%
2.052.412.059,33 USD
1.765.655.251,51 USD
1.521.533.084,14 USD
2.054.998.247,30 USD
1.767.880.105,11 USD
1.523.450.326,11 USD
0,1260%
0,1260%
0,1260%
2.052.412.059,33 USD
1.765.655.251,51 USD
1.521.533.084,14 USD
2.053.328.527,50 USD
1.766.443.673,52 USD
1.522.212.497,73 USD
0,0447%
0,0447%
0,0447%
2.052.412.059,33 USD
1.765.655.251,51 USD
1.521.533.084,14 USD
2.052.514.611,32 USD
1.765.743.475,25 USD
1.521.609.109,93 USD
0,0050%
0,0050%
0,0050%
2.052.412.059,33 USD
1.765.655.251,51 USD
1.521.533.084,14 USD
2.058.463.349,19 USD
1.770.861.073,44 USD
1.526.019.141,25 USD
0,2948%
0,2948%
0,2948%
Change
With Change
Difference
Income Taxes
Without
Change
With Change
Difference
Emissions
Without
Compliance
Change
Costs
With Change
Difference
Transportation
Without
Costs
Change
With Change
Difference
Source: CERI, 2017. Study No. 163 and author’s own calculations
The following chart describes better the results:
44
Graph 19
Costs
0.294838%
0.284845%
0.166297%
0.126007%
0.071466%
0.006439%
0.044653%
0.004997%
Source: CERI, 2017. Study No. 163 and author’s own calculations.
The analysis shows how a 1% change in every cost affects total revenues for the entire period.
First, as it is expected, an increase of 1% of any cost affects in the same way the three
scenarios. For example, if Fixed Capital rises in 1%, it will increase the total costs in 0,2848%
for each of the three scenarios: high, reference and low. Second, transportation and fixed
capitals are the variables that affect the most total revenues, 0,2848% and 0,2948%
respectively. Third, an extra one percent expense in royalties and taxes reduce total revenues
in almost 0,17%. Fourth, emission compliance costs and operating working capital basically
don’t affect total NPV in any proportion and finally, other operating costs (including
electricity) change in 0,17% total revenues.
6. Global Contribution
The increase of oil sands production due to different increases in technology is compared with
the total global production for each of the three scenarios. Two kinds of annual increases were
considered: 1% and 5%.
45
Graph 20
High Case Scenario
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
6.0%
5.5%
5.0%
4.5%
4.0%
3.5%
3.0%
2.5%
2.0%
1%
5%
Source: CERI, 2017. Study No. 163 and author’s own calculations
Graph 21
Reference Case Scenario
5.0%
4.5%
4.0%
3.5%
3.0%
2.5%
0%
2034
2035
2036
2030
2031
2032
2033
2026
2027
2028
2029
2022
2023
2024
2025
2019
2020
2021
2016
2017
2018
2.0%
5%
Source: CERI, 2017. Study No. 163 and author’s own calculations
46
Graph 22
Low Case Scenario
4.0%
3.5%
3.0%
2.5%
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2.0%
0%
5%
Source: CERI, 2017. Study No. 163 and author’s own calculations
As it can be seen, the difference among each of the percentages is very small. From a general
perspective, the final contribution from each scenario to global production differs in the
following amounts:
1. In the high case scenario, the production will equal 6% of total global production by
2036.
2. In the reference case scenario, the production will equal 5% of total global
production by 2036.
3. In the low case scenario, the production will equal 4% of total global production by
2036.
7. Model Equilibrium
It is mathematically logical to assume the existence of an equilibrium point where the total
Net Present Value for the period 2016-2036 takes a value close to zero (+/- 2%). The bigger
the ratio of technology (Aα), the bigger the value of NPV; while the bigger the ratio of the
discount rate (sigma, δ), the lower the value of NPV. This is explained because Aα is located
in the numerator and Sigma is located in the denominator of the equation and both of them are
positive.
Therefore, to measure how much must the discount rate change for every value of Technology
in order to have a zero-profit (+/- 3%) in the long run, the following assumptions were done:
1. Years: 2016-2036
47
2. Investment (thousands CAD):
3. Investment (thousands USD): Using a constant exchange rate of 1,2 USD/CAD
4. Oil price increase: it was assumed this variable increases annually at a 4% rate
(IEO2016 Reference case).
5. Oil price benchmark: WTI was selected; whose vale equals 92% of Brent Price
forecast.
6. Quantity of Oil produced: Taken from CERI “Reference Case Scenario”
7. Technology (A) increases at a rate called alfa (α), which starts from 1% and ends at
6%, At = At-1 + At-1* α.
8. Sigma value: Changes for each value of Technology. The method used to calculate it
was interpolation.
Annex 1 can be seen in order to know the data and equations used to calculated the values.
Next table shows the combination of Aα and sigma (δ) that creates a value for NPV within
$100,000,000.
Table 7
Technology
0,0%
0,1%
0,2%
0,3%
0,4%
0,5%
0,6%
0,7%
0,8%
0,9%
1,0%
1,1%
1,2%
1,3%
1,4%
1,5%
1,6%
1,7%
1,8%
1,9%
2,0%
Sigma
9,16%
9,76%
10,36%
10,94%
11,51%
12,07%
12,63%
13,17%
13,71%
14,24%
14,76%
15,27%
15,78%
16,28%
16,78%
17,27%
17,76%
18,24%
18,72%
19,19%
19,66%
-
-
NPV
1.397.368,18 USD
82.006.620,44 USD
8.727.757,62 USD
39.246.738,59 USD
60.362.561,58 USD
80.854.169,94 USD
4.248.363,16 USD
46.092.345,93 USD
6.955.286,59 USD
7.215.277,59 USD
5.431.817,65 USD
46.183.068,61 USD
30.723.096,93 USD
49.576.799,77 USD
22.268.966,34 USD
33.049.852,81 USD
5.228.962,62 USD
17.564.145,96 USD
2.986.999,42 USD
17.500.863,98 USD
9.448.180,48 USD
Source: CERI, 2017. Study No. 163 and author’s own calculations
48
Graph 23
Relation between Technology and
Discount Rate
Discount Rate
19%
17%
15%
13%
Discount Rate
11%
9%
Technology Rate
Source: CERI, 2017. Study No. 163 and author’s own calculations
Next table shows the same relation for a bigger scale (technology grows until 6%).
Table 8
Technology
0,0%
0,5%
1,0%
1,5%
2,0%
2,5%
3,0%
3,5%
4,0%
4,5%
5,0%
5,5%
6,0%
Discount Rate
9,16%
12,07%
14,76%
17,27%
19,66%
21,95%
24,16%
26,32%
28,43%
30,50%
32,54%
34,56%
36,55%
-
NPV
1.397.368,18 USD
80.854.169,94 USD
5.431.817,65 USD
33.049.852,81 USD
9.448.180,48 USD
10.779.648,21 USD
36.723.209,43 USD
12.590.533,39 USD
4.679.029,22 USD
6.219.914,07 USD
5.465.117,37 USD
10.289.597,70 USD
5.898.297,20 USD
Source: CERI, 2017. Study No. 163 and author’s own calculations
Graph 9
49
Relation between Technology and
Discount Rate
Discount Rate
39%
34%
29%
24%
19%
Discount Rate
14%
6.0%
5.5%
5.0%
4.5%
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
9%
Technology Rate
Source: CERI, 2017. Study No. 163 and author’s own calculations
The table shows that in order to maintain the zero NPV equilibrium, an increase of 0,1% in
Technology should be corresponded by a decrease of 0,56 (in average) in the value of Sigma.
In brief, calculations found that investments in heavy oil are fundamentally affected by
improvements in technology. Addition of new techniques and methodologies to explore,
extract and transport oil is core for the project’s revenues. Fluctuation in the exchange rate
between USD and CAD and between WTI and Brent benchmarks don’t have big effects on
revenues. Moreover, findings show that increases in discount rate (due to increases in
inflation, for example) negatively affects revenues, for which an increase in technology rate is
proposed in order to maintain the goal of profits. Under certain assumptions, a table of values
for technology and discount rate is proposed. It is important to mention that technology has a
cost, which was not considered in this document and might be a topic for future researches.
50
CONCLUSIONS
The relevance of this investigation’s topic is highlighted when considering outlooks from
powerful oil companies, such as British Petroleum, Total or Exxon Mobil, which consider
non-conventional oil fields as key for the future energy production and consumption. The
importance increases when taking into account the dimension of global investment in energy
during the next two decades.
Despite the vast amount of academic literature related to energy investments, the field of
specifically economic literature focused on investment optimization for heavy oil sector has
not reached its peak. Traditional investment models applied to this industry lack of dynamism
and are built in a rigid way, as a consequence their utility is limited when including external
shock in oil prices, supply and demand. Although previous researches have addressed to the
importance of improving investment models, the novelty of this research relies on its intention
to develop a more appropriate economical approach which could guide to the development of
a new investment optimization model that can be successfully applied to non-conventional
oil, using oil sands in Alberta, Canada. The key point in this paper was to examine the
influence of technology in investments’ revenues and dynamics.
Analysis showed that exchange rate and oil benchmarks fluctuations are not a considerable
threat for the model, due to the fact that there is low volatility in the spread among WTI and
Brent oil benchmarks, as well as Canadian and US Dollars. On the other hand, increases in
technology ratios applied to production highly affect total income and revenues, meaning that
the future of economic investments in non-conventional oil is highly related to technological
advances.
The consequences of increment in costs were also analyzed with a sensitivity analysis, finding
that those related with production and transportation are more important than other ones, such
as royalties, taxes or emission compliance. This means that technology improvement might
reduce more investment’s expenses than legal or political costs.
Regarding the discount rate, which is one of the most important factors when building
investment models, it was found that technology can neutralize the harmful effects of
increases in discount rate. With this is mind, a simulation was done with the aim of obtaining
the equilibrium among discount rate and technology. Therefore, analysts can know how much
technology must change when opportunity costs vary in order to maintain the equilibrium.
One of the most important challenges that affected this investigation was confidentiality: too
much data about costs and strategies are kept in secret by companies and governments.
51
Besides, companies do not specifically present in their financial reports detailed data for their
activities in heavy-oil fields. In addition, although the main framework of this research is
economical, the complexity of the topic makes necessary to consider and analyze data and
researches from politics, geopolitics, management, geology, physics, chemistry and
engineering.
In a more specific economic aspect, there is not a generally accepted methodology for
forecasting oil prices and predicting oil shocks. Therefore, there is a lot of uncertainty when
developing models for income and revenue in the industry, in particular if it’s a long run
forecast. Equilibrium prices tend to vary due to external and company-internal reasons,
making impossible to identify one general oil equilibrium price or one discount rate for the
entire industry. On the other hand, the quantity of oil produced is not affected just by financial
or economic reasons, but also for qualitative-managerial, legal or environmental reasons;
mining the accuracy of any economic approach developed.
52
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ANNEX 1
Total values were assumed for 2016, Technology rate was stablished in 1% and discount rate
in 5%.
Table 1: Amount of Investment:
Year
Investment (thousands CDN)
Investment (USD)
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
35.838.260,79 CAD
37.578.670,41 CAD
40.934.351,31 CAD
47.967.528,72 CAD
51.015.939,59 CAD
58.463.347,48 CAD
57.255.359,75 CAD
53.036.756,66 CAD
54.591.965,61 CAD
57.086.730,13 CAD
53.176.647,64 CAD
49.661.678,37 CAD
49.775.149,26 CAD
56.362.667,66 CAD
59.348.531,30 CAD
57.024.143,80 CAD
55.621.496,59 CAD
54.987.959,52 CAD
55.446.769,05 CAD
29.865.217.326,98 USD
31.315.558.673,57 USD
34.111.959.423,70 USD
39.972.940.603,35 USD
42.513.282.989,72 USD
48.719.456.234,59 USD
47.712.799.793,10 USD
44.197.297.216,41 USD
45.493.304.678,41 USD
47.572.275.104,80 USD
44.313.873.034,74 USD
41.384.731.974,33 USD
41.479.291.049,14 USD
46.968.889.719,75 USD
49.457.109.415,94 USD
47.520.119.833,68 USD
46.351.247.155,59 USD
45.823.299.598,46 USD
46.205.640.872,11 USD
56
2035
2036
TOTAL
54.367.753,36 CAD
54.860.632,80 CAD
1.094.402.339,80 CAD
45.306.461.131,90 USD
45.717.194.000,60 USD
912.001.949.830,88 USD
Source: CERI, 2017. Study No. 163 and author’s own calculations
Where:
1. CDN: Canadian Dollars
2. USD: United States Dollars
3. Exchange rate USD/CAD: 1,2 for the entire period.
Table 2: Income
Income
Year
Oil Price WTI
Quantity Bbl Day
Quantity Bbl Year
Index Of
Technology
2016
54,00 USD
2.227.316,64
812.970.572,61
1,00
2017
56,16 USD
2.759.921,75
1.007.371.439,31
1,01
2018
58,41 USD
2.953.571,41
1.078.053.566,01
1,02
2019
60,74 USD
3.078.927,79
1.123.808.643,95
1,03
2020
63,17 USD
3.165.044,80
1.155.241.353,76
1,04
2021
65,70 USD
3.244.884,32
1.184.382.775,22
1,05
2022
68,33 USD
3.315.535,47
1.210.170.448,24
1,06
2023
71,06 USD
3.368.636,16
1.229.552.200,19
1,07
2024
73,90 USD
3.447.512,42
1.258.342.033,95
1,08
2025
76,86 USD
3.519.853,42
1.284.746.499,31
1,09
2026
79,93 USD
3.680.186,61
1.343.268.114,35
1,10
2027
83,13 USD
3.824.842,77
1.396.067.609,53
1,12
2028
86,46 USD
4.055.749,81
1.480.348.679,37
1,13
2029
89,91 USD
4.304.807,18
1.571.254.620,78
1,14
2030
93,51 USD
4.520.345,68
1.649.926.174,82
1,15
2031
97,25 USD
4.714.756,34
1.720.886.063,78
1,16
2032
101,14 USD
4.900.229,56
1.788.583.790,47
1,17
2033
105,19 USD
5.016.622,90
1.831.067.357,26
1,18
57
2034
109,39 USD
5.118.983,84
1.868.429.101,43
1,20
2035
113,77 USD
5.211.819,28
1.902.314.038,92
1,21
2036
118,32 USD
5.274.860,53
1.925.324.094,54
1,22
TOTAL
81.704.408,71
29.822.109.177,82
Source: CERI, 2017. Study No. 163 and author’s own calculations
Where:
1. Starting year: 2016
2. Starting price WTI: 54 USD
3. Days in a year: 365
4. Annual increase of Technology: 1%
Table 3: Total annual income (without using discount rate)
TOTAL INCOME
Year
Total Income with
Total Income without
Technology
Technology
2016
37.396.646.339,88 USD
37.396.646.339,88 USD
2017
48.674.576.153,19 USD
48.192.649.656,62 USD
2018
54.715.150.524,43 USD
53.637.045.901,81 USD
2019
59.912.071.951,20 USD
58.150.066.777,77 USD
2020
64.691.826.153,02 USD
62.167.573.381,75 USD
2021
69.666.417.770,46 USD
66.285.206.087,07 USD
2022
74.770.907.827,10 USD
70.437.577.454,16 USD
2023
79.797.226.063,80 USD
74.428.313.465,25 USD
2024
85.781.619.357,77 USD
79.217.886.274,26 USD
2025
91.995.734.756,71 USD
84.115.363.948,27 USD
2026
101.034.028.158,30 USD
91.464.787.671,79 USD
2027
110.297.616.262,45 USD
98.862.369.441,71 USD
2028
122.850.918.896,51 USD
109.023.952.797,82 USD
2029
136.966.913.631,22 USD
120.347.704.345,53 USD
2030
151.073.512.630,14 USD
131.428.361.672,09 USD
58
2031
165.512.441.650,58 USD
142.564.054.693,29 USD
2032
180.693.500.553,10 USD
154.099.259.214,17 USD
2033
194.308.712.680,60 USD
164.069.902.580,15 USD
2034
208.266.438.377,10 USD
174.114.348.448,16 USD
2035
222.730.449.332,66 USD
184.362.883.207,59 USD
2036
236.785.955.329,45 USD
194.056.620.343,79 USD
Total
2.497.922.664.399,67 USD
2.198.422.573.702,93 USD
Source: CERI, 2017. Study No. 163 and author’s own calculations
Where:
1. Total Income with Technology is the result of multiplying: oil price WTI * oil barrel
production per year * Index of Technology
2. Total Income without Technology is the result of multiplying: oil price WTI * oil
barrel production per year
59
Table 4. Total other costs
Year
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
TOTAL
Operating Working Capital Fuel (Natural Gas)
Royalties
Income Taxes
309.945.030,81 USD 3.439.881.735,34 USD 6.065.099.209,38 USD 2.149.290.951,33 USD
384.060.361,24 USD 4.262.440.402,58 USD 7.515.410.675,36 USD 2.663.238.242,68 USD
411.007.922,04 USD 4.561.514.151,18 USD 8.042.728.791,42 USD 2.850.104.115,14 USD
428.452.045,51 USD 4.755.115.324,73 USD 8.384.080.737,50 USD 2.971.069.102,45 USD
440.435.766,12 USD 4.888.114.978,09 USD 8.618.581.849,60 USD 3.054.169.329,00 USD
451.545.933,05 USD 5.011.419.617,65 USD 8.835.988.995,96 USD 3.131.211.961,99 USD
461.377.483,39 USD 5.120.533.709,14 USD 9.028.375.781,59 USD 3.199.388.122,55 USD
468.766.776,32 USD 5.202.542.747,06 USD 9.172.971.726,85 USD 3.250.628.629,26 USD
479.742.900,44 USD 5.324.359.731,14 USD 9.387.755.882,43 USD 3.326.741.752,25 USD
489.809.602,86 USD 5.436.083.625,22 USD 9.584.744.195,92 USD 3.396.548.557,56 USD
512.120.968,60 USD 5.683.703.208,86 USD 10.021.339.828,13 USD 3.551.265.077,33 USD
532.250.776,13 USD 5.907.111.072,83 USD 10.415.246.061,95 USD 3.690.853.742,70 USD
564.382.934,01 USD 6.263.725.349,59 USD 11.044.017.960,06 USD 3.913.671.821,09 USD
599.040.824,17 USD 6.648.371.114,16 USD 11.722.214.160,42 USD 4.154.004.403,68 USD
629.034.354,15 USD 6.981.250.127,21 USD 12.309.136.733,40 USD 4.361.992.324,68 USD
656.087.811,82 USD 7.281.499.157,39 USD 12.838.527.071,69 USD 4.549.592.531,13 USD
681.897.570,12 USD 7.567.945.163,42 USD 13.343.580.320,15 USD 4.728.568.396,05 USD
698.094.429,96 USD 7.747.703.755,42 USD 13.660.525.429,92 USD 4.840.884.325,77 USD
712.338.594,92 USD 7.905.790.635,41 USD 13.939.259.608,76 USD 4.939.659.436,89 USD
725.257.227,34 USD 8.049.166.277,19 USD 14.192.055.361,20 USD 5.029.242.740,40 USD
734.029.811,04 USD 8.146.527.575,02 USD 14.363.719.963,64 USD 5.090.075.574,94 USD
11.369.679.124,04 USD 126.184.799.458,63 USD 222.485.360.345,33 USD 78.842.201.138,85 USD
Emissions Compliance Costs Abandonment Costs Transportation Costs
240.503.794,40 USD
298.014.050,80 USD
318.924.179,94 USD
332.460.057,17 USD
341.758.900,49 USD
350.379.904,34 USD
358.008.757,61 USD
363.742.525,89 USD
372.259.518,38 USD
380.070.839,38 USD
397.383.483,83 USD
413.003.334,49 USD
437.936.484,31 USD
464.829.491,98 USD
488.103.160,05 USD
509.095.460,54 USD
529.122.704,68 USD
541.690.759,86 USD
552.743.609,17 USD
562.767.903,18 USD
569.575.044,63 USD
8.822.373.965,10 USD
22.017.953,01 USD
27.282.976,48 USD
29.197.284,08 USD
30.436.484,11 USD
31.287.786,66 USD
32.077.033,50 USD
32.775.449,64 USD
33.300.372,09 USD
34.080.096,75 USD
34.795.217,69 USD
36.380.178,10 USD
37.810.164,42 USD
40.092.776,73 USD
42.554.812,65 USD
44.685.500,57 USD
46.607.330,89 USD
48.440.810,99 USD
49.591.407,59 USD
50.603.288,16 USD
51.521.005,22 USD
52.144.194,23 USD
807.682.123,57 USD
14.191.417.558,06 USD
17.584.927.687,47 USD
18.818.772.561,65 USD
19.617.484.641,03 USD
20.166.181.881,54 USD
20.674.881.819,92 USD
21.125.037.887,17 USD
21.463.370.594,60 USD
21.965.933.130,09 USD
22.426.856.078,65 USD
23.448.424.021,21 USD
24.370.105.208,90 USD
25.841.336.634,27 USD
27.428.213.473,93 USD
28.801.523.789,20 USD
30.040.217.350,93 USD
31.221.965.792,38 USD
31.963.569.555,24 USD
32.615.765.501,76 USD
33.207.269.441,92 USD
33.608.938.725,32 USD
520.582.193.335,24 USD
Total Other Costs
26.418.156.232,31 USD
32.735.374.396,60 USD
35.032.249.005,45 USD
36.519.098.392,51 USD
37.540.530.491,50 USD
38.487.505.266,40 USD
39.325.497.191,09 USD
39.955.323.372,07 USD
40.890.873.011,47 USD
41.748.908.117,29 USD
43.650.616.766,06 USD
45.366.380.361,44 USD
48.105.163.960,06 USD
51.059.228.280,98 USD
53.615.725.989,26 USD
55.921.626.714,38 USD
58.121.520.757,79 USD
59.502.059.663,77 USD
60.716.160.675,08 USD
61.817.279.956,44 USD
62.565.010.888,83 USD
969.094.289.490,76 USD
60
Where values come from the multiplication of: barrels of oil produced each year with average costs.
Table 5: Total Revenues year by year (without using discount rate)
REVENUES
Year
Total Revenue With Technology
Total Revenue Without Technology
2016
-18.886.727.219,41 USD
-18.886.727.219,41 USD
2017
-15.376.356.916,98 USD
-15.858.283.413,55 USD
2018
-14.429.057.904,72 USD
-15.507.162.527,34 USD
2019
-16.579.967.044,65 USD
-18.341.972.218,09 USD
2020
-15.361.987.328,20 USD
-17.886.240.099,47 USD
2021
-17.540.543.730,53 USD
-20.921.755.413,92 USD
2022
-12.267.389.157,09 USD
-16.600.719.530,04 USD
2023
-4.355.394.524,68 USD
-9.724.307.123,22 USD
2024
-602.558.332,11 USD
-7.166.291.415,62 USD
2025
2.674.551.534,62 USD
-5.205.819.273,82 USD
2026
13.069.538.357,50 USD
3.500.297.870,99 USD
2027
23.546.503.926,68 USD
12.111.257.105,95 USD
2028
33.266.463.887,30 USD
19.439.497.788,61 USD
2029
38.938.795.630,48 USD
22.319.586.344,79 USD
2030
48.000.677.224,94 USD
28.355.526.266,90 USD
2031
62.070.695.102,52 USD
39.122.308.145,23 USD
2032
76.220.732.639,72 USD
49.626.491.300,80 USD
2033
88.983.353.418,37 USD
58.744.543.317,92 USD
2034
101.344.636.829,91 USD
67.192.546.900,97 USD
2035
115.606.708.244,32 USD
77.239.142.119,25 USD
2036
128.503.750.440,02 USD
85.774.415.454,36 USD
Total
616.826.425.078,03 USD
317.326.334.381,29 USD
Source: CERI, 2017. Study No. 163 and author’s own calculations
60
61
Where:
1. Total Income with Technology is the result of: oil price WTI * oil barrel production per year
* Index of Technology - Investment
2. Total Income without Technology is the result of: oil price WTI * oil barrel production per
year - Investment
Table 6: Present Value of Revenues (using a discount rate of 5%)
PRESENT VALUE OF REVENUES
Year
Sigma
With Technology
Without Technology
2016
0,05
-18.886.727.219,41 USD
-18.886.727.219,41 USD
2017
0,05
-14.644.149.444,74 USD
-15.103.127.060,52 USD
2018
0,05
-13.087.580.865,96 USD
-14.065.453.539,54 USD
2019
0,05
-14.322.398.915,58 USD
-15.844.485.233,20 USD
2020
0,05
-12.638.344.992,63 USD
-14.715.053.994,55 USD
2021
0,05
-13.743.474.986,95 USD
-16.392.742.815,26 USD
2022
0,05
-9.154.114.665,55 USD
-12.387.712.508,56 USD
2023
0,05
-3.095.297.574,04 USD
-6.910.883.520,93 USD
2024
0,05
-407.835.197,09 USD
-4.850.428.109,86 USD
2025
0,05
1.724.039.766,10 USD
-3.355.717.520,12 USD
2026
0,05
8.023.562.815,33 USD
2.148.879.254,34 USD
2027
0,05
13.767.153.176,32 USD
7.081.201.194,65 USD
2028
0,05
18.524.011.862,90 USD
10.824.639.759,28 USD
2029
0,05
20.650.074.691,26 USD
11.836.553.176,21 USD
2030
0,05
24.243.603.788,40 USD
14.321.467.607,73 USD
2031
0,05
29.857.065.634,70 USD
18.818.499.134,64 USD
2032
0,05
34.917.595.836,88 USD
22.734.467.460,90 USD
2033
0,05
38.823.142.348,94 USD
25.630.049.664,82 USD
2034
0,05
42.110.789.862,87 USD
27.919.891.090,51 USD
2035
0,05
45.749.500.111,19 USD
30.566.151.347,42 USD
61
62
2036
Total
0,05
TOTAL
48.431.712.050,58 USD
32.327.475.084,33 USD
226.842.328.083,53 USD
81.696.943.252,88 USD
Source: CERI, 2017. Study No. 163 and author’s own calculations
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62