Shale Gas Argentina - A Systems Dynamic Approach.pdf

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SHALE GAS IN ARGENTINA

A SYSTEM DYNAMICS APPROACH

Natalie Ballew, Kumar Das, Jeanne Eckhart, Stephen Hester, Reed Malin, John Maxwell, & Colin Meehan

Decision Pathways, Fall 2012



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Abstract

A premier hydrocarbon formation, Argentina's Vaca Muerta shale play has estimated recoverable

reserves of 741 million barrels of oil and 4.5 trillion cubic feet of natural gas. Though a material

contribution to any nation's petroleum reserves, Argentina's development of newly identified

shale resources is especially important to offset, or even reverse, the nation's declining domestic

energy production and debilitating energy trade imbalance. Growing reliance of fuel imports

from Bolivia and Trinidad are the result of inadequate infrastructure investment, discouraging

development policy, and low-set price controls. Due to trends toward nationalism and the recent

expropriation of foreign assets, Argentina will struggle to exploit its shale resources

independently and may be forced to leverage the expertise and capital of multinational firms.

To analyze both the impact to the Argentine economy and potential profitability for

multinationals, we determined the key variables involved in developing the Vaca Muerta and

incorporated them into a system dynamics model. Outputs include net profits, jobs created, and

tax revenue generated. Among the components used in the model's 24-year run-time are

available geological findings from actual test wells in the region, extensive cost data from shale

formations throughout the United States and Eastern Europe, current Argentine tax code and

regulatory constraints, and commodity prices adjusted annually through probabilistic methods.

Certain variables, such as the degree local and federal governments incentivize production and

the number of wells drilled, are modifiable by the user. In spite of an upfront infrastructure cost

of $500 million, the user's ability to manipulate several variables, and the complex interaction

between the system's numerous random and looping variables, the model consistently yields

multibillion dollar profits provided a reasonable number of wells drilled. Lack of data and the



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inherent difficulty of predicting public policy limit the model's accuracy, but it serves as a useful

tool in gauging the feasibility and challenges associated with developing the Vaca Muerta.

Keywords: system dynamics, natural gas, energy policy



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1. Introduction

Argentina has a long history of gas use; in fact, a coal gas distribution system lit the city of

Buenos Aires in 1856 (Maciel, 1992). Natural gas comprises nearly half of total energy

consumption and about one-third of electrical power. For most of its history, Argentina was

relatively self-sufficient when it came to the production of natural gas. However, domestically

produced gas has declined 10% since peaking in 2006 as demand has risen by greater than 20%

over the same period (EIA, 2012). From 2008 to 2011, the volume of imported liquid natural gas

(LNG) increased by 900% at a price of $15 per million British thermal units (MMbtu),

contributing to a large deficit in the energy trade balance (Gerold, 2012).

In 2009, the world’s fourth largest shale play, the Vaca Muerta formation, was discovered

in the Neuquén Basin in western Argentina. Development of these reserves has the potential to

radically impact the nation’s economy, integration with global markets, and geopolitics. If fully

developed, based on estimates by Exxon, Chevron, and YPF1, oil and natural gas produced from

the Vaca Muerta shale formation could meet Argentina’s energy needs for over fifty years.

Development, however, requires a seismic shift in national policy on multiple fronts and a

multibillion-dollar infusion of capital and expertise from international firms (Gonzalez, 2012).

In spring of 2012 Repsol, a Spanish oil and gas company with operations in Argentina,

had its ownership stake in YPF partially seized (EIA, 2012) by the federal government. The

official rationale behind the expropriation was to pressure other oil companies to grow

production and penalize underinvestment (Ruano, 2012).

International oil and gas firms play a critical role in developing and implementing

advanced drilling and recovery techniques, in addition to providing the capital necessary for

1 Argentina’s state-controlled energy company



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development. It is unlikely Argentina would efficiently produce petroleum and natural gas from

its shale formations without extensive involvement and support by foreign companies.

Argentina’s recent attitude toward international firms, such as Repsol, compromises the nation’s

ability to secure vital expertise and capital. Preserving an equitable environment and upholding

contracts with remaining energy companies is in sharp contrast to growing nationalistic

tendencies. Outside of the failure to attract sufficient investment, extraction rates from the Vaca

Muerta may fall short of expectations. Without long-term commitment from the Argentine

people, development will be at constant risk.

Despite massive production from the Barnett, Eagle Ford, and Marcellus in the United

States (U.S.), extracting hydrocarbons from shale formations is relatively new technology with

successful applications generally limited to the continental U.S. In contrast to conventional

reservoirs, recovery from low permeability and porosity rock of shale formations has not been

readily duplicated overseas (Carroll, 2012).

In efforts to create a usable tool for firms considering investment in the Vaca Muerta, we

use system dynamics modeling. Our model incorporates varying levels of foreign direct

investment (FDI), tax burden, and other variables multinationals consider illustrating their

impact on profitability. We first describe the past and present economic, political, and oil and gas

environments in Argentina to gauge the value of developing of the Vaca Muerta. Next, we

discuss the interactions, components, and structure of the system dynamics model created to

analyze the impacts of development. We close with model results and general recommendations.

2. Background

Development of the Vaca Muerta may have a profound effect on Argentine affairs. Recent

economic progress has increased energy demand by 35% since 1998, the year domestic oil



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production peaked. Declining reserves and increasingly arduous policy have led to Argentina’s

current multibillion-dollar annual energy trade imbalance. While Argentina remains the top

producer of natural gas in South America, development of shale oil a viable solution to reversing

the nation’s downward hydrocarbon production trend (EIA, 2012). However, the current political

scene presents significant uncertainty to the future of development for the Vaca Muerta.

2.1. Politics

Argentina’s current political climate can best be described as turbulent. Since the

financial collapse in 2000, Argentina has struggled to regain political and economic stability.

The result has been increasingly nationalistic and protectionist economic policies. This has

included increased restrictions on foreign direct investment and imports of many essential goods,

which would be essential for any development. This section provides a brief overview of the

political constraints that will affect any development in the Neuquén basin.

2.1.1. Regulatory Structure

Argentina is a federal republic with 23 provinces; each province has its own set of

regulatory statutes. All provinces have their own legal structure, linked and in compliance with

the national constitution. From a development perspective, national projects such as

infrastructure or tax structures are set at the national level, whereas labor and environmental

regulations are a hybrid of local and federal agencies. Importantly, for exploitation of onshore

resources, the individual provinces handle the concession and permit process. (Bravo, 2009)

2.1.2. Recent History

Beginning in 1999 and continuing for nearly 3 years, Argentina experienced a complete

financial collapse. The country experienced hyperinflation and ultimately defaulted on its entire

foreign debt—the largest credit default in history. The legacy of this economic instability is still



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highly present in Argentine politics. This extreme shift paved the way resurgence in nationalism

and a return to protectionist trade and economic development policies. This resurgence is

perhaps best understood through the political actors that emerged from this crisis—Nestor

Kirchner and his wife Cristina Fernandez.

Nestor Kirchner was elected to the presidential office in 2003 and oversaw the most

significant part of the financial recovery. Under his tenure, the Argentine economy stabilized

and began to add significant jobs in the industrial and agricultural sectors. Key to this was

government support of Import Substituting Industrialization—a strategy whereby strong barriers

and taxes are placed on imports of specific goods in order to force purchases of local equivalents.

Following his death in 2007, Nestor Kirchner was succeeded by his wife, who continued to

implement similar economic strategies. Since 2010 the Fernandez government has more

aggressively applied these tactics. In June of 2012, the government levied a new 14% tariff on all

capital goods imported into the country (Morales and Castilla, 2012). This accompanies an

already existing list of some 600 specific goods—including high-tech products like computers—

that have specific import restrictions. These actions have led to lawsuits against the Argentine

government in the World Trade Organization.

These protectionist policies have been implemented largely on the back of a wave of

popular support and nationalistic political sentiments. The center-left leaning Fernandez has, for

most of her first and second terms, enjoyed high approval ratings. She was easily reelected to a

second term, however since her reelection she has faced public scrutiny as inflation continues to

rise and many fear a repeat of the financial disaster of 2001. In the face of this increased scrutiny

and pressure—both from within and abroad—Fernandez has stuck with her policies and driven

an even harder nationalistic line. This has manifested itself through international sparring over



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the contested Falkland Islands and most recently the re-nationalization of YPF—the former

National Oil Company.

This nationalist political discourse has had major impact on the growing Argentine

energy sector. Though the Falklands conflict has its roots in the abortive armed struggle between

the UK and Argentina, recent offshore oil discoveries have upped the stakes for political control

of the island (Milmo, 2012). But most importantly the re-nationalization of YPF means that the

Argentine state has directly invested itself in growing and promoting the country’s oil and gas

reserves. This expropriation—which was met with popular approval at home and nearly

universal condemnation internationally—will ultimately place the responsibility and impetus for

development of the Vaca Muerta field in the central government’s hands. The Fernandez

government now has the difficult task of enticing the same foreign investors that have been

suffering under import restrictions to support YPF.

2.2. Economics

Argentina historically produced oil and gas through state owned companies, YPF and

Gas del Estado. In the 1980s and 1990s, modest reforms took place in the oil and gas industry.

First, service contracts allowed upstream companies to either participate or pay commitment

contracts. Between 1985 and 1990, Argentina allowed for a tax stabilization scheme during

which 76 contracts were signed (Maciel, 1992). In 1992, the gas market was partially

deregulated, leading to the privatization of Gas del Estado (Ennis, 2003). The natural monopoly

of gas utilities was divided into four areas: production, local distribution, long-range distribution,

and commercialization. The distribution systems were not privatized but production and

commercialization were. A gas regulator, ENARGAS, was created to regulate the distribution



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system and retail side of the gas market (Ennis, 2003, Ponzo et al. 2010). Repsol became the

largest shareholder of YPF in the years after denationalization (Herrera and Garcia, 2003).

This fragmentation negatively impacted gas market functionality as oil and gas

production declined. Argentina previously prided itself on maintaining a robust domestic energy

industry and for successfully exploiting its resources. From 1971 through 2004, Argentina

increased overall production by about 725%. In 2004, natural gas production peaked at

1,892,272,637 MMBtu. From 2006 through 2011, production continued to fall and by 2011 was

down over 10% from peak production. Natural gas demand continued to grow about 2% per year

as GDP grew at 7% (IEA, 2012)

2

.

The aforementioned supply and demand imbalance was remedied through imports from

Bolivia and Trinidad (EDI, 2012). Gas imports increased from 3,354,502 MMBtu in 2003 to

273,168,720 MMBtu in 2011, an increase of 800%. Amplified reliance on imports shifted the

energy component of the trade balance negative; a major change from Argentina’s historical

precedent (Gerold, 2012). Besides electricity consumption, other top sectors of natural gas

consumption are industrial and residential, which use 28% and 24%, respectively (EIA, 2012).

Energy production is often a chief economic driver for countries endowed with substantial

resources. The inverse also holds true; significant constraints on economic growth may occur if

demand is met by costly fuel imports. Argentina was a net hydrocarbon exporter until 2004 when

supply disruptions foreshadowed the extent of declining production. Inadequate natural gas

supplies interrupted electricity generation and industrial sectors (Recalde, 2011). Proper

development of the Vaca Muerta shale formation may lead to an increasing production of natural

gas and economic stabilization.

2 In our model we assume a constant GDP. See Appendix C.



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Figure 1: Argentina data from 1971 to 2010. From top left across then bottom left across: (a) GDP growth, (b) Population increase, (c) Natural gas production and imports vs. exports, and Crude oil production and imports vs. exports (Data from EIA, 2012)



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Another important aspect of the economic situation for the natural gas industry in

Argentina is price controls initiated during the financial crisis in 2001 and 2002 (EIA, 2012,

Ponzo et al. 2011). The policy was designed to put downward pressure on prices and alleviate the

extreme inflation in the post-crisis period. The cap on gas prices was set at $2.50 per MMBtu

and kept prices low in many areas. Following the initial success, however, the lack of incentives

for producers caused debilitating gas shortages during peak demand periods. Winter and summer

extremes usually coincided with insufficient supplies (EIA, 2012, Gonzalez, 2012).

Though only initial development has begun in Vaca Muerta, there has been a great deal

of speculation as to the potential economic impact of the field. The level of this impact will

largely depend on the investment by foreign firms, government infrastructure build-up, and

actual productivity of new wells. Because of this uncertainty, Argentina and its potential partners

are looking to analogues in the U.S. where similar booms have occurred. Using metrics in

relation to the Marcellus shale in Pennsylvania, the Vaca Muerta formation could have

immediate impacts ranging in the tens of billions of dollars (Considine et al., 2011).

2.3. Oil and Gas

The discovery of the Vaca Muerta formation significantly increased oil and natural gas

reserves in Argentina. Estimates vary, but according to the Energy Information Administration

(EIA), Argentina has approximately 2.5 billion barrels of oil in proven reserves, with the

Neuquén basin containing 25%; this region is the second most productive oil province in

Argentina. Figure 1 illustrates the vast natural gas resources in Argentina and the extent of the

Neuquén Basin. The Vaca Muerta shale formation is predicted to have about 741 million barrels

of recoverable oil. Proven natural gas reserves in Argentina are nearly 13.4 trillion cubic feet,

despite a 50% decrease from ten years ago. 42% of proven and 50% of recoverable gas reserves



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are in the Neuquén basin. With an estimated 4.5 trillion cubic feet of recoverable natural gas, the

Vaca Muerta is among the most promising formations in Argentina’s history (EIA, 2012).

Figure 2: Shale gas resources in Argentina (EIA ,2012)

2.4 Infrastructure: transportation, energy, and utilities

Infrastructure and its expansion are essential for supporting hydrocarbon production.

Road transport is the major means of transportation in Argentina. About 30% of Argentina’s

200,000 km of roadways are paved and 80% of cargo is transported via roads. Recently, bids

have been submitted for highway construction in the Neuquén province (BMI, 2012a). Rail

transport is also a key element of Argentina’s infrastructure. The nation’s length of operating

track has declined from its peak shortly after World War II, but still serves over 500 million

passengers annually. Swedish construction company, Skanska, is scheduled to complete a project



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extending the railway line between Neuquén and Port of Bahia Blanca. (BMI, 2012a). 25.2

million tons or 12.5% of all freight was transported via rail in 2007. Modernization efforts are

lowering costs, raising efficiencies, and increasing total rail capacity (INDEC, 2010). A well-

functioning rail system will aid the movement of heavy machinery and personnel essential to

develop the Vaca Muerta.

In response to the adverse effect inadequate infrastructure and resource development has

on its economy, the Argentinean government recently allocated $4 billion towards construction

of gas pipelines and a re-gasification plant. Plans are also in place to build additional power

plants and further diversify energy resources. These investments are beneficial but will not solve

Argentina’s larger problem of insufficient domestic energy production and the resulting energy

trade imbalance (BMI, 2012b).

The fluctuating presence of U.S. oil and gas majors ExxonMobil, EOG Resources, and

Apache, and others has also alleviated infrastructure constraints (Morris, 2012). Apache plans to

spend significant funds in 2012 for drilling and development of formations in Neuquén Basin. In

recent developments, YPF and Chevron have reached an agreement to develop shale gas

formations in Argentina. YPF plans to invest $7 billion annually through 2017 to increase

hydrocarbon production. (BMI, 2012b).

3. Methodology

To fully incorporate the complex interactions relevant to developing the Vaca Muerta

formation, we use a system dynamics approach. The model seeks to understand the development

of the Vaca Muerta over a twenty four-year period, incorporating variables pertaining not only to

the formation’s geology and drilling parameters but also the political and economic environment.



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To create a model we used Forio, an online software that allows users to create

simulations using a system dynamics methodology. Forio allows input of variables, properties,

and decisions using straightforward language. The end product is an online simulation through

which players can explore the modeled system. This kind of simulation can aid firms and

governments in determining whether development in the Vaca Muerta is worthwhile.

First, each group member created an initial model of how we envisioned key variables

interacting (see appendix for the drawn out images). We analyzed the individual models as a

team to determine where each overlapped and what new interactions emerged from the

collective. This method proved effective in considering the system from several perspectives

given each member of the team differs in background. The interdisciplinary nature of our group,

consisting of geologists, economists, and policy makers, was vital toward modeling the

complexity of developing the Vaca Muerta.

Figure 3: Initial conceptual model progress

Next, we identified key variables and decisions and established the initial relationships of

the model within Forio. Throughout the coding process we evaluated the logic and feasibility of

outputs. From this iterative process we added micro-interactions or sub modules to create a more

nuanced and dynamic model. Final primary components include the following:



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TotalInfrastructureCost , TotalCost , TotalProfit , generated TaxRevenue, and ImportRegulation.3

Each consists of multiple sub modules; for instance, total profit interacts with nine other

components.

Figure 4: Model interactions in Forio

3.1 Model Structure

Forio allows iterative programming; we used one-year increments between 2012 and

2036. Key to system dynamics modeling, variables can store past calculations and engage in

loops. Model behavior is structured in such a way that users can alter certain variables (decision

variables, such as tax regime) prior to simulation. In addition, the number of wells (the

determining factor in production rates and subsequently profitability, is modifiable at each step

in the simulation.

3.2. Assumptions

To create a manageable model and because of limited access and existence of

information, we made assumptions concerning the formation geology, the oil and gas industry,

3 See Appendix A for more information on variables and decision variables used in the model.



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the costs of development and production, and the policies of the Argentine government. All

projections, however, are rooted in generally well-documented activities related to shale

production and exploration throughout the United States and Poland.4 It is not possible to cover

all variables and associated assumptions in detail here, but we made significant efforts to deal

with uncertainty and improve model reliability. For instance, projected commodity prices are

treated as random variables constrained by the historically accurate commodity costs from data

going back several decades. Over the 24-year period the model simulated, probabilistic

approaches were implemented when possible. We determined this method optimal in dealing

with uncertainty given the availability of sufficient historical data to calculate dependable

figures.

3.2.1. Formation Geology

For the simplicity of our model, we assume the Vaca Muerta formation is a uniform

reservoir with no variability in production across different wells. Given the already limited

information on the topic, our model does not account reserve growth in either oil or gas reserves

in the Vaca Muerta; the life of the Vaca Muerta formation is complete when all estimated

reserves are extracted. We assume annual declination rates of 77% and 89% (Swindell, 2012) for

natural gas and associated liquids respectively. Rates change over time, but given the model’s

time horizon, the impact on outcomes is minimal in comparison to the complexity of modeling

more accurate and time-sensitive decline curves.

Because of limited exploratory data, our model assumes no existing active wells in

formation. Given the size of estimated reserves, the development of a few wells should not

meaningfully alter the model’s results. For the wells we add in our model, we assume vertical

4 Poland has had recent experience with a similar production scenario (rural and undeveloped region) as the VacaMuerta.



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wells and horizontal wells produce the same amount of oil and gas. We found wells in shale

formations across the U.S. produce 100 to 600 barrels of oil per day and 500 to 6000 MMBtu of

natural gas. Therefore, oil production for wells in this model is 350 barrels per day and we

agreed upon an equivalent gas capacity of 1,944 MMBtu per day (Seeley, 2012). The model’s

time horizon and nature of its outputs allows us to assume oil and gas are extracted

simultaneously. Annual well capacity estimation assumes 365 days in a year. In the code,

estimated means recoverable reserves and liquids means only oil, not natural gas liquids. Figures

within the model are estimations of recoverable oil and gas reserves; a significant increase in the

recoverability factor or rate would necessitate minor model adjustments.

3.2.2. Costs

The cost structure used in the model is vital to determine reliable profitability numbers

and realizes several objectives. First, variables adjust to project size, which is achieved by

focusing expenses down to the individual well level. This design allows model outputs, such as

profitability, to adjust automatically if number of wells changes. Second, the cost structure

incorporates available data while minimizing the impact of any individual presumption. Data

comparable to what U.S.-based oil companies regularly provide on their operations is not widely

available on Argentina’s existing oil production. Specific cost estimates for developing the Vaca

Muerta shale formation do not exist. Our approach was to leverage the significant volume of data

on existing shale projects and fit it to the Vaca Muerta’s geology and geography while

accounting for Argentina’s infrastructure and domestic resources in the areas of heavy

equipment, labor costs, and supply, as well as the pace of development.

Activity in the Barnett, Eagle Ford, Marcellus, and other shale plays in the U.S. offer

insight into well completion time frames, decreases in production costs over time, well pattern



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density, infrastructure constraints and their effect on development, among other factors. Per well

development costs began in the $8-13 million range and plateau at the $2.5-5 million depending

on firm and location (Cowan, 2011). This array of data from years of quarterly earnings reports

helped us identify the majority of cost variables and trends in how they changed over time.

Announcements made by firms following the first series of wells drilled in an area revealed

general consensus that drilling costs would decrease significantly, often by 50%, within the 24

months after initial drilling (EOG Resources, 2004). Crew and equipment relocation costs, road

building, and economies of scale contribute to this phenomenon. Given many of these

projections occurred in 2007 and 2008 as shale development accelerated, we were able to

confirm these forecasts and establish a reliable cost decline curve. Within the code, primary cost

variables include TotalInfrastructureCost , PerWellDrillingCost , and TotalDrillingCost . These

variables are affected by diverse circumstances, such as GeneralInflationRate, TaxRegime, and

an initial infrastructure cost of $500 million.5

Many parameters of Vaca Muerta formation development, however, cannot be accurately

estimated using data from existing projects. For instance, there is no way to ascertain if logistical

costs and expediency are comparable. Even relatively unpopulated regions of the continental

U.S., such as North Dakota and Wyoming, have access to multiple interstate highways connected

to the rest of the country. Specialized labor and most equipment is available domestically, if not

locally. Argentina’s comparably smaller oil sector would need infusions of foreign personnel and

equipment to fully develop a shale play of Vaca Muerta’s magnitude. No comparable situation

exists to help predict the logistical, and potentially more important, political complications

multinational firms would likely encounter. Many of the same firms interested in the Vaca

5 Water is not considered a development cost or limitation to development; for our purposes we assume free and abundant water.



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Muerta, such as ExxonMobil and Statoil, experimented in Poland’s recently discovered shale

reserves. If Exxon’s experience in Poland is any indication, local population and transportation

issues will be major hurdles (Carroll, 2011). Argentina’s current political environment magnifies

these risks considerably. Development costs averaged $11 million per well and upfront

infrastructure costs are an estimated $500 million. In the model, we use this upfront costs based

on the conditional similarities between the Poland’s shale play and the Vaca Muerta (Carroll,

2011).

3.2.4 Policies

We assume that despite the vast area the Neuquén Basin covers, there will be consistent

regulatory statutes across the region. Our model includes several policy choices regarding

potential policy options at the federal and local levels. These options include tariffs on imported

equipment, taxes on profits generated by foreign investments and local incentives to encourage

local economic development resulting from well-drilling activities. Policy options are decisions

made by the user. Import regulation impacts the cost of drilling and infrastructure as a tariff on

imported equipment. Tax regime is a reflection of both the level of acceptance of foreign

investment and domestic performances regarding resource protection; this influences tax

revenue. Local tax incentives reflect local community willingness to support development; this

directly affects per well drilling cost. Policy options are informed by existing regulation and

current discussion regarding the development of natural gas in Argentina (Ernst and Young,

2010)

In the case of tariffs on imported equipment the user may select a business as usual

scenario which marginally increases the cost of drilling and infrastructure based on increased

equipment costs; the decreased regulation scenario reduces this impact to a lesser degree; the



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tariff free scenario prices drilling and infrastructure costs at the baseline costs discussed

elsewhere in this paper. If the user chooses local incentives the per well drilling cost is reduced,

reflecting the benefit of local incentives.6

Another policy decision variable is federal tax policy, which may be seen as a reflection

of both the level of federal acceptance of foreign investment and domestic preferences regarding

resource protection. The tax rate is applied to total profit net of costs: the resource protection

scenario sets the highest rate, business as usual sets a moderate rate and opportunity sets the rate

at zero. These levels affect both the profitability of the venture and the domestic benefits of

permitting foreign production.

4. Model development

In an attempt to model the intricacies of this system we employed several of Forio’s

mathematical operations. To assess the long-term viability of resource production from shale

using decline curves, we used the stock function. This function enables the modeling of dynamic

flows (i.e. flows that change over time) to address this challenge.

The stock function is structured to begin with an initial value set by the user; in our

model this value is the initial number of wells multiplied by the initial annual well output for gas

and liquids. At each iteration of the model the user chooses whether to add additional wells and

any additional wells produce at the expected initial year production capacity. Flows accumulate

as wells are added, while production from old wells taper off quickly at the aforementioned rates,

reflecting the realistic pattern of production from shale resources.

6 See Appendix A for policy option values.



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The gas and liquids price in the model have been set to vary and float based on a

stochastic formula with a random number generator. This change in gas prices seeks to model the

variability that has existed within the Neuquén basin over the past ten year (Gerold, 2012). This

model price assumption is also replicated within the liquids products as well. There is literature

which supports the stochastic volatility to this type of modeling. As stated before, this model is

simply expressed and hopes to approach a mean-regression model, which can be approximated

over our model period. This stochastic behavior is also applied to domestic gas demand. 7

5. Results and Sensitivity Testing

The purpose of this model is to assist in decision making around the exploitation of the Vaca

Muerta Shale play and, as is the definition of a system dynamics model, there is no definitive

result from these types of models. To account for the lack of tangible results we conducted a

basic sensitivity test on a few potential common outcomes from running the model, trialing four

basic scenarios: Business as Usual (BAU), BAU with Tax Incentives, All incentives, and

Negative incentives. Each year it was assumed a static number of wells would be added,

meaning a flat linear growth of total well capacity in Vaca Muerta. Also, incentives were held

constant year-to-year, assuming a fixed policy path for development at the outset.

This sensitivity testing revealed the following results on the total net profit, as described

in Figure 4. With other factors held constant, Tax Incentives equal yielded the highest profit,

more than combining incentives. This result is surprising, given that Free Trade incentives were

perceived as being very important when constructing the model. Ultimately, raw taxes had a

greater effect on total profit than combinations of alternative incentives.

7 See Appendix B for a more detailed description of using the stock function and stochastic price volatility.



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Figure 4: Profit Scenarios Sensitivity Testing

6. Conclusion

The model’s output is centered on overall drilling profit from all drilling companies on

producing oil and natural gas from the Vaca Muerta shale formation. Aggregate profit is

independent of the number of firms involved but is constrained by the estimated recoverable

resources, a half-billion dollar up front infrastructure cost, taxes, foreign investment incentives,

domestic demand, export potential, commodity prices, a maximum of wells that can be drilled

during a given time period, and the rate of production, among other variables.

The model proved flexible in exploring basic macro scenarios and in all cases returned

positive profits; this is unsurprising given the nature of shale gas extraction and production. The

strength of this model is its ability to test multiple scenarios on a yearly basis—as the political

situation in Argentina is in a constant state of flux. While the removal of a tax incentive or a

trade barrier may have marginal impact (if programmed in at the beginning of analysis), if these

incentives fluctuate from year-to-year the total profit—and the total tax revenue—will be



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affected. This is critical for international companies looking to invest and create risk profiles.

The total economic impact of development of Vaca Muerta is a multi-faceted problem;

the model presented here is a simplification for the purposes of identifying critical factors in

development. The purpose of the model is not to be comprehensive, but rather to provide

stakeholders with a baseline assessment of Vaca Muerta that highlights key economic indicators.

Through use of this model, stakeholders and investors can begin to evaluate the value of the field

and the varied approaches to its development available for selection.



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[Accessed 28 Nov 2012]. Available at <http://www.eia.gov/countries/cab.cfm?fips=AR>.

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24

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2012].

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Presentation at the SPE Annual Technical Conference.



25

Appendix A: Table of key variables used in the model code

Variable Name Type8

Description

ImportRegulation D

Policy option selected by user, user selects different levels on tariffsof important drilling and pipeline equipment. Business as usual

increases cost of drilling and infrastructure by 5%. Decreased regulation reduces this impact to 1.5%. Tariff free prices drilling and infrastructure costs at the baseline costs.

LocalTaxIncentive DPolicy option selected by user, allows user to reduce the price of welldrilling by 2% through local incentives.

TaxRegime D

Policy option selected by user, includes stringent Resource

Protection (30% tax), Business as Usual (15% tax) and Opportunity (0% tax). These levels affect both the profitability of the venture and the domestic benefits of permitting foreign production. Tax isimposed on all profits.

WellstoAdd D Number of wells added each year by user

DeclineGasCurve V Gas production decline rate for a single well

DomesticGasDemand V Modeled stochastically, based on historical demand

EstimatedGasResource V Total estimated resource, declines annually as production grows

ExportPrice V Price paid for exported gas

GasProduction VAnnual amount of gas produced, a function of production from newwells drilled in the current year and last year's production with thedecline rate applied

GeneralInflation V Inflator applied to per well drilling cost

PerWellDrillingCost V Cost to drill a single well

TotalAfterTaxProfit VA function of domestic and export revenues, drilling and infrastructure costs, with the selected TaxRegime applied

TotalDrillingCost V PerWellDrillingCost *WellstoAdd

TotalInfrastructureCost VEstimated cost of pipelines and other infrastructure needs to supportoil and gas production

WellGasCapacityYear VEstimated production capacity of a gas well in the first year of production

8 V indicates a variable. D indicates a decision variable, changeable by the user during model simulation.



26

Appendix B: Expanded description of applying STOCK function and floating commodity prices

STOCK :

The STOCK function is structured to begin with an initial value set by the user; in our model this

value is the initial number of wells multiplied by the initial annual well output for gas and

liquids. At each iteration of the model the user chooses whether to add additional wells or not;

any additional wells produce at the expected initial year production capacity. Total production

for that year is the combination of the initial or previous year’s production, and the production

from wells added in the current year. Through the STOCK function the total production for the

current year, n, is multiplied by the decline rate and used as the prior year production for the year

n+1. As wells are added these flows accumulate, while production from old wells taper off

quickly, reflecting the real life pattern of production from shale mineral resources.

Floating gas prices:

The gas and liquids price in the model have been set to vary based on a stochastic formula with a

random number generator. For example, in the model, the gas price is set at $5.2 per MMBtu, the

price quoted in a G&G assessment for gas delivered to the at Neuquén province. The initial part

of the formula is: V GasPriceInitial = 5.2. The next part of the price formula operates under the

assumption of a forward price curve; the prices going forward are set to a percentage change in

each year. This is given by: V GasPrice = NORMINV ( RANDBETWEEN (0,1),1.2,.5)*

GasPriceInitial. The mean price change is 20% with a standard deviation of 50%. This change in

gas prices seeks to model the variability that has existed within the Neuquén basin over the past

ten years (Gerold 2012). The price assumption is also replicated within the liquids products.

Literature supports the stochastic volatility to this type of modeling. Schwartz (1997) and

Deng (2002) present more complex models of stochastic volatility than our model. As Deng



27

states: “…several mean-reversion jump-diffusion models…describe spot prices of energy

commodities that may be very costly to store. I incorporate multiple jumps, regime-switching

and stochastic volatility into these models in order to capture the salient features of energy

commodity prices due to physical characteristics of energy commodities.” This underlies aspects

of our intent to set price initially and allow for volatility and variability in future projections of

crude oil and natural gas prices. The mean regression is established in our model by

incorporating a percentage change on a year-to-year basis rather than trying to project a direct

price for several years into the future, which, with large standard deviations, would lead to

negative prices. As stated before, this model is simply expressed and hopes to approach a mean-

regression model that can be approximated over the model time-span.



28

Appendix C: Model Code in Forio Language

########### Nat ur al Gas Devel opment Model ########

###################################

# Model proper t i es

M St ar t Ti me = 2012 M EndTi me = 2036

M Ti meStep = 1

M Execut eDeci si onI mmedi atel y = TRUE

###################################

R Resource = Gas, Li qui ds

#### Model Deci si ons ####

D Wel l sSt ar t = 10

V I ni t i al Numberof Wel l s = Wel l sSt ar t

D Wel l st oAdd = 0

V NoMor eWel l s = I f ( Est i matedGasResour ce <= 0, 1, 0)

V Numberof Wel l s = STOCK ( Wel l st oAdd, I ni t i al Numberof Wel l s)

P Numberof Wel l s. Label = Wel l s

P Numberof Wel l s. Deci si onMi n = 0

P Numberof Wel l s. Deci si onMax = 500

D Decl i neGasCurve = 0. 77

D Decl i neLi qui dsCur ve = 0. 89

D Pr i ce[ Resource] = {2. 5, 8}

V GasPri ceI ni t i al = 5. 2

V GasPri ce = NORMI NV( RANDBETWEEN( 0, 1) , 1. 2, . 5) * GasPri ceI ni t i al

P GasPr i ce. Label = USD

V Li qui dsPri ce = NORMI NV( RANDBETWEEN( 0, 1) , 8, 10)

P Li qui dsPr i ce. Documentat i on = www. f orbes. com/ 2009/ 07/ 16/ george- mi t chel l -

gas- busi ness- energy- shal e. ht ml , www. sl i deshar e. net / Mar cel l usDN/ t he-

economi c- i mpact - of - t he- val ue- chai n- of - a- mar cel l us- shal e- wel l

P Li qui dsPr i ce. Label = USD

D Expor t Pr i ce[ Resource] = {10, 0}

P Expor t Pr i ce. Label = USD

D TaxRegi me = I F( Resour cePr otect i on,



29

I F( Bi zUsual Tax,

I F( FI Opp, 1, 0) ,

0) ,

0)

V Resour ceProt ect i on = 0. 7

V Bi zUsual Tax = 0. 85

V FI Opp = 0. 98

# Thi s i s i nt ended t o be a t ax on al l pr of i t s t o r ef l ect publ i c pol i cy

D I mport Regul at i on = I F(Tar i f f Free,

I F( DecReg, I

F( Bi zUsual Reg, 1, 0) ,

0) ,

0)

V Tar i f f Fr ee = 1. 00 V DecReg = 1. 5

V Bi zUsual Reg = 1. 75 # Thi s i s a tax on i mpor t ed equi pment as ref l ected i n t he cost of

i nf r ast r uctur e bel ow

D Local TaxI ncent i ve = I F( I ncent i ves,

I F( NoI ncent i ves, 1, 0) ,

0)

V I ncent i ves = 0. 98

V NoI ncent i ves = 1 # l ocal i ncent i ves f or l ocal i t i es t o pr ovi de housi ng, et c. ; i ncent i ve t hat

onl y ef f ect s t he per wel l cost

D I ni t i al Domest i cGasDemand = 1600000000

D Domest i cLi qui dsDemand = 100000

#t hese number s ar e i n t he r i ght uni t s ( bar r el s/ day)

V RandomDemandFact or = RAND

V DemandGr owt h =NORMI NV( RANDBETWEEN( 0, 1) , 0. 03, 0. 08)

V GasDemand = I ni t i al Domest i cGasDemand * DemandGr owt h

V Li qui dDemand = Domest i cLi qui dsDemand * DemandGr owt h

#Randomi zi ng demand

D Gener al I nf l at i onRat e = 0. 02

### I nput Var i abl es ###

V Wel l Li qui dsCapaci t yDay = 350 * ( Wel l st oAdd)



30

P Wel l Li qui dsCapaci t yDay. Label = Bar r el s/ day

P Wel l Li qui dsCapaci t yDay. Documentat i on = go. gal egr oup. com/ ps/ i . do?act i on=

i nt er pr et &i d=GALE%7CA282514322&v=2. 1&u=t xshracd2598&i t =r &p=AONE&sw=w&aut h

Count

V Wel l GasCapaci t yDay = 1944 * ( Wel l st oAdd)

P Wel l GasCapaci t yDay. Label = MMBtu/ day V Wel l Li qui dsCapaci t yYear = Wel l Li qui dsCapaci t yDay * 365

P Wel l Li qui dsCapaci t yYear. Label = Bar r el s/ year

V Wel l GasCapaci t yYear = Wel l GasCapaci t yDay * 365

P Wel l GasCapaci t yYear . Label = MMBtu/ Year V Domest i cGasDemand = STOCK ( DemandGr owt h*Domest i cGasDemand, I ni t i al Domest i cG

asDemand) #These f unct i ons det ermi ne the annual wel l out put of gas & l i qui ds based on

dai l y out put

V Per Wel l Dr i l l i ngCost = STOCK ( Gener al I nf l at i onRat e*Per Wel l Dr i l l i ngCost , I ni t l

Per Wel l Dr i l l i ngCost ) * Local TaxI ncent i ve

V I ni t i al Per Wel l Dr i l l i ngCost = 9000000

P Per Wel l Dr i l l i ngCost . Label = USD

P Per Wel l Dr i l l i ngCost . Documentat i on = www. f orbes. com/ 2009/ 07/ 16/ george-

mi t chel l - gas- busi ness- energy-

shal e. ht ml , shal e. t ypepad. com/ haynesvi l l eshal e/ dr i l l i ng-

cost s/ , i nf o. dr i l l i ngi nf o. com/ urb/ barnet t / , www. i nvest opedi a. com/ st o

ck- anal ysi s/ 2009/ dri l l i ng- and- compl et i on- cost s- cont i nue- t o- f al l - i n- shal e-

pl ays- swn- gxmr -

cl r 1002. aspx#axzz29pvwgbeq, www. sl i deshare. net / Mar cel l usDN/ t he-economi c- i mpact - of - t he- val ue- chai n- of - a- mar cel l us- shal e- wel l

V I ni t i al Est i mat edGasResource = 4500000000

P I ni t i al Est i mat edGasResource. Documentat i on = www. ei a. gov/ count r i es/ cab

. cf m?f i ps=AR

P I ni t i al Est i mat edGasResource. Label = MMBt u V Est i mat edGasResource = STOCK ( -

GasPr oduct i on, I ni t i al Est i mat edGasResource, NONNEGATI VE)

P Est i mat edGasResource. Label = MMBt u

P Est i mat edGasResource. Document at i on = decl i ne over t i me i n gas r esour ces

V I ni t i al Est i matedLi qui dsResour ce = 741000000

P I ni t i al Est i matedLi qui dsResour ce. Deci si onMi n = 0

P I ni t i al Est i matedLi qui dsResour ce. Documentat i on = www. ei a. gov/ count r i es/

cab. cf m?f i ps=AR



31

P I ni t i al Est i matedLi qui dsResour ce. Label = Barr el s of oi l V Est i matedLi qui dsResour ce = STOCK ( -

Li qui dsPr oduct i on, I ni t i al Est i matedLi qui dsResource, NONNEGATI VE)

P Est i matedLi qui dsResour ce. Label = Bar r el s of Oi l

P Est i matedLi qui dsResour ce. Documentat i on = decl i ne over t i me i n associ at e

d l i qui ds r esour ces V I ni t i al GasPr oduct i on = Wel l GasCapaci t yYear*Wel l sSt ar t V GasPr oduct i on = I F( STOCK( ( -

Decl i neGasCurve*GasPr oduct i on) +( Wel l st oAdd*Wel l GasCapaci t yYear ) , I ni t i al GasP

r oduct i on, NONNEGATI VE) >Est i mat edGasResource, Est i mat edGasResource,

STOCK ( ( -

Decl i neGasCurve*GasPr oduct i on) + ( Wel l st oAdd*Wel l GasCapaci t yYear) , I ni t i al Ga

sPr oduct i on, NONNEGATI VE) )

V I ni t i al Li qui dsPr oduct i on = Wel l Li qui dsCapaci t yYear *Wel l sSt ar t V Li qui dsPr oduct i on = I F( STOCK( -

Decl i neLi qui dsCur ve*Li qui dsPr oduct i on+Wel l st oAdd*Wel l Li qui dsCapaci t yYear, I ni

t i al Li qui dsPr oduct i on, NONNEGATI VE) >Est i matedLi qui dsResour ce, Esti

matedLi qui dsResour ce, STOCK ( -

Decl i neLi qui dsCur ve*Li qui dsPr oduct i on+Wel l st oAdd*Wel l Li qui dsCapaci t yYear, I ni

t i al Li qui dsPr oduct i on, NONNEGATI VE) )

V Tot al Pr of i t = ( Tot al Revenue - Tot al Cost )

P Tot al Pr of i t . Label = USD

V Tot al Af t erTaxProf i t = Tot al Pr of i t - TaxRevenue

P Tot al Af t erTaxProf i t . Label = USD

V Tot al Revenue = GasRevenue + Li qui dsRevenue

P Tot al Revenue. Label = USD

V I ni t i al GasRevenue = 0 V GasRevenue = STOCK ( GasProduct i on * GasPr i ce, I ni t i al GasRevenue, NONNEGATI V

E)

P GasRevenue. Label = USD

V PerWel l GasOut put = ( Est i matedGasResour ce) / Number of Wel l s

P PerWel l GasOutput . Label = MMBt u/ wel l

V I ni t i al Li qui dsRevenue = 0

V Li qui dsRevenue = STOCK ( ( Li qui dsPr oducti on * Li qui dsPr i ce) , I ni t i al Li qui dsRevenue, NONNEGATI VE)

P Li qui dsRevenue. Label = USD

V Per Wel l Li qui dsOut put = ( Est i matedLi qui dsResour ce) / Numberof Wel l s

P Per Wel l Li qui dsOut put . Label = Bar r el s of Oi l / Year

V I ni t i al Expor t Revenue = 0 V Expor t Revenue = STOCK ( ( GasExpor t Pot ent i al * G



asPr i ce) + ( Li qui dsExpor t Pot ent i al * Li qui dsPr i ce) , I ni t i al Expor t Revenue, NO

NNEGATI VE)

P Expor t Revenue. Label = USD

V GasExport Pot ent i al = GasPr oduct i on - Domest i cGasDemand

V Li qui dsExport Pot ent i al = Tot al Li qui dsPr oduct i on - Domest i cLi qui dsDemand

V Tot al Cost = Tot al I nf r ast r uct ureCost + Tot al Dr i l l i ngCost

P Tot al Cost . Label = USD V Tot al I nf r ast r uct ureCost = ( 500000000 + ( 200000 * Number of Wel l s) ) * I mpor t Re

gul at i on P Tot al I nf r ast r uct ureCost . Label = USD

P Tot al I nf r ast r uct ureCost . Documentat i on = Houst on Busi ness J our nal ( J un 30, 2

011) – Repor t : Shal e pi pel i ne cost s t r i pl e si nce 2004

V I ni t i al Tot al Dr i l l i ngCost = 9000000*Wel l sSt ar t V Tot al Dr i l l i ngCost = STOCK ( Per Wel l Dr i l l i ngCost * Wel l st oAdd, I ni t i al Tot al Dr

i l l i ngCost )

P Tot al Dr i l l i ngCost . Label = USD

V Tot al Li qui dsPr oduct i on = Numberof Wel l s * Wel l Li qui dsCapaci t yYear

P Tot al Li qui dsPr oduct i on. Label = Bar r el s Oi l / Year

V TaxRevenue = ( 1 - TaxRegi me) * Tot al Pr of i t

P TaxRevenue. Label = USD

V TaxRevGDP = TaxRevenue/ 445990000000

V Di r ect J obsPer Wel l = ( Number of Wel l s * 11. 95)

V I ndi r ect J obsPer Wel l = Di r ect J obsPer Wel l * 3. 04 V Tot al J obsPer Wel l = Di r ect J obsPer Wel l + I ndi r ect J obsPer Wel l

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