MARIO KHATER

ASSESSING THE ECONOMIC FEASIBILITY OF SHALE GAS A North American Perspective

An Economic Insight into Production Decline Curve Analysis

Mémoire présenté à la Faculté des études supérieures et postdoctorales de l’Université Laval

dans le cadre du programme de maîtrise en économique pour l’obtention du grade de Maître ès arts (M.A.)

DÉPARTEMENT D’ÉCONOMIQUE FACULTÉ DES SCIENCES SOCIALES

UNIVERSITÉ LAVAL QUÉBEC

2013

© Mario Khater, 2013

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Résumé Ce mémoire étudie la faisabilité économique de la production de gaz de shale à partir de cinq formations géologiques différentes (le Marcellus, le Barnett, le Haynesville, le Montney et l’Utica) dispersées aux États-Unis et au Canada. Depuis 1990, le progrès technologique, notamment en termes de forage horizontal et de fracturation hydraulique, a permis la production économique du gaz naturel à partir de shales et a amélioré les perspectives à long terme pour l’approvisionnement en gaz naturel en Amérique du Nord. L'Energy Information Administration (EIA) prévoit que d'ici 2046 près de 50% de l'approvisionnement en gaz naturel américain proviendra du gaz de shale; d'autres chercheurs estiment que l'approvisionnement en gaz naturel en Amérique du Nord, sous forme de gaz de shale, durera plus de 100 ans. Ainsi, ce gaz non conventionnel est censé révolutionner les perspectives futures du développement énergétique. Cependant, une fois exploité, sa mise en valeur reste incertaine vue que sa rentabilité économique est vulnérable et dépend de plusieurs facteurs économiques et géologiques. Notre projet déterminera l’état général de la production de la ressource via l’interprétation des courbes de déclin et l’analyse des économies de seuils de rentabilité et aura deux volets: (1) technique et (2) économique. Premièrement, dans la partie technique, on analyse les courbes de déclin afin de prédire comment est ce que les réserves de gaz de shale sont estimées dans l'industrie. Deuxièmement, dans la partie économique, qui, par l'évaluation des tendances à la baisse de la production de gaz de shale, nous permettra d’identifier les divers agrégats qui rendent cette production, économiquement rentable. En conclusion, on développe un modèle logistique de déploiement des puits pour le shale d'Utica illustrant l'impact potentiel des volumes de gaz produits, le temps de déploiement des puits, le nombre de puits forés, ainsi que des redevances versées sur la rentabilité économique du projet.

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Abstract This thesis analyzes the economic feasibility of producing shale gas from five different shale formations (Marcellus, Barnett, Haynesville, Montney, and Utica) dispersed in the United States (U.S.) and Canada. Since 1990, advances in technology, mainly horizontal drilling and hydraulic fracturing have allowed economic production of natural gas from shales and boosted the long-term outlook for the supply of natural gas in North America. The Energy Information Administration predicts that by 2046 almost 50% of the U.S. natural gas supply will come from shale gas; other researchers estimate that the natural gas supply in North America, in the form of shale gas, will last more than 100 years. Thus, shale gas is thought to be the game changer of the course of future energy development trends; however, its economic profitability is vulnerable and depends upon several economic and technical factors. Our study is conducted through Decline Curve Analysis and breakeven economics and has two facets: (1) technical and (2) economic. First, the technical part consists on investigating Production Decline Curve Analysis in order to understand how shale gas reserves are estimated in the industry. Second, comes the economic part, which by assessing steep initial decline trends of gas production from five major shale plays, focuses on identifying both geologic and economic aggregates that render shale gas production, economically profitable. Finally, to better understand the economic functionality of producing shale gas, we develop a logistic growth model of wells deployment for the Utica shale that depicts the potential impact of volumes of gas produced, wells deployment time, number of wells drilled, and royalties paid on the project economics.

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Acknowledgments I would like to thank many persons who contributed to the accomplishment of my master degree project.

First of all, I would like to express my gratitude to my supervisor, Pr Patrick González. I am thankful for your valuable insights and directions that gave me needful guidance to complete the research and write my thesis. I also thank you for being there during my entire project. I thank you for your patience and persistence with the help and assistance that you offered me. I also thank you because you made the most difficult tasks so easy to accomplish and to understand.

I would also like to thank my friend Rana Daher for all the support, help and encouragement she gave me.

Finally, I thank my beloved family who supported me during all my life; I would not be able to succeed without you. I thank you for believing in me. I thank you for the love and care you gave me. I thank my mother Norma for all the efforts she made for me. I thank you for being there for me and with me at all times despite the great distances that separated us. I also thank my father Elia for the trust and the guts you built in me and the great generosity and modesty that I carved through you. I thank my three brothers Georges, Rami and Hicham for their love and support. Finally, to my cousin Ziad, I thank you for the infinite generosity, the kindness and the gracious hospitality that you extended to me during my entire stay at your place.

“Natural gas is the best transportation fuel. It's better than gasoline or diesel. It's cleaner, it's cheaper, and it's domestic. Natural gas is 97%

domestic fuel, North America. ”

𝑇ℎ𝑜𝑚𝑎𝑠 𝐵.𝑃𝑖𝑐𝑘𝑒𝑛𝑠 (1928−)

“Natural gas is the future. It is here.”

𝐵𝑖𝑙𝑙 𝑅𝑖𝑐ℎ𝑎𝑟𝑑𝑠𝑜𝑛 (1947−)

𝑇𝑜 𝑚𝑦 𝑏𝑒𝑙𝑜𝑣𝑒𝑑 𝑓𝑎𝑚𝑖𝑙𝑦,

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Table of contents

Résumé ........................................................................................................................................... II

Abstract .......................................................................................................................................... III

Acknowledgments .......................................................................................................................... IV

List of Figures ............................................................................................................................... VIII

List of Tables .................................................................................................................................. IX

Abbreviations .................................................................................................................................. X

Table of conversion ......................................................................................................................... X

Introduction .................................................................................................................................. 11

Literature Review .......................................................................................................................... 13

Chapter I: Shale Gas and Production Decline Curve Analysis ...................................................... 14

1.1. Definition: What is Shale Gas? ......................................................................................... 14 1.2. How Shale Gas is produced? ............................................................................................ 15 1.2.1. Horizontal Drilling ............................................................................................................ 15

1.2.2. Hydraulic Fracturing ......................................................................................................... 16

1.3. Understanding Production Decline Curve Analysis .......................................................... 17 1.3.1. History of Reserves Estimates Calculation Methods ....................................................... 17

1.3.2. PDCA in unconventional reservoirs .................................................................................. 20

1.3.2.1. Exponential truncation of hyperbolic equations ............................................................. 21

1.3.2.2. Multiple transient hyperbolic exponents ........................................................................ 21

Chapter II - The economics of shale gas wells .............................................................................. 23

2.1. Model, Methodology, Variables ....................................................................................... 25 2.1.1. Cash flow analysis ............................................................................................................ 25

2.1.1.1. Explaining the model variables ........................................................................................ 25

2.1.2. Sensitivity analysis ............................................................................................................ 27

2.1.3. Decision making parameters ............................................................................................ 28

2.1.3.1. Net Present Value ............................................................................................................ 28

2.1.3.2. Internal Rate of Return (IRR) ........................................................................................... 29

2.1.3.3. Payout Period ................................................................................................................... 30

VII

2.2. Shale plays and model assumptions: ............................................................................... 30 2.2.1. Barnett .............................................................................................................................. 31

2.2.2. Marcellus .......................................................................................................................... 35

2.2.3. Haynesville ....................................................................................................................... 37

2.2.4. Montney ........................................................................................................................... 39

2.2.5. Utica ................................................................................................................................. 41

2.3. Economic discussion ......................................................................................................... 42 2.4. A logistic distribution model of wells deployment (Utica Shale) ..................................... 46 2.4.1. Model and Assumptions .................................................................................................. 47

2.4.2. Economic discussion ........................................................................................................ 50

Conclusion ..................................................................................................................................... 54

Appendix ....................................................................................................................................... 55

References .................................................................................................................................... 88

VIII

List of Figures Figure 1: Conventional, tight, and shale gas and oil. ................................................................................ 14

Figure 2: Stages of a shale gas production process. ................................................................................. 16

Figure 3: DCA, rate versus cumulative gas production. ............................................................................ 21

Figure 4: Energy trends in the U.S. ............................................................................................................ 23

Figure 5: Natural gas production by source, 1990-2030 (tcf) ................................................................... 23

Figure 6: Shale Gas Production Economics (Banks 2008) ......................................................................... 24

Figure 7: Comparison of number of wells drilled (per year). .................................................................... 46

Figure 8: Truncation of hyperbolic equations ........................................................................................... 56

Figure 9: Arps Type curves ........................................................................................................................ 56

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List of Tables

Tables 1, 2 (a, b, c, d, and e): Shale plays models parameters and expected ranges ........................... 31-41

Table 3: Sensitivity Analysis Results ........................................................................................................... 43

Table 4: Estimating gas reserves using Arps equations ............................................................................. 58

Table 5: Quebec’s New Royalty Regime (NRR) .......................................................................................... 61

X

Abbreviations

bcf billion cubic feet

EIA Energy Information Administration

EUR Estimated Ultimate Recovery

FYP First Year Price

GP Gas Produced

IRR Initial Rate of Return

md Millidarcy

Mmcf Million cubic feet

NPV Net Present Value

OGIP Original Gas in Place

PDCA Production Decline Curve Analysis

RF Recovery Factor

TRR Technically Recoverable Resources

mcf Thousand cubic feet

tcf trillion cubic feet

U. S. United States

Table of conversion

tcf bcf Mmcf mcf

1 tcf 1 1,000 1,000,000 1,000,000,000

1 bcf 0,001 1 1,000 1,000,000

1 Mmcf 0.000001 0.001 1 1,000

1 mcf 0.000000001 0.000001 0.001 1

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Introduction

Shale gas reservoirs differ from conventional gas reservoirs in the fact that it is the process of making a well ready for production that forms the reservoir (T.A. Blasingame 2008). Since their permeability is very low, a multi-stage conductive platform is required between the well completion and the reservoir to attain commercial economic rates. To achieve the latter, massive multi-stage hydraulic fracture techniques are used to boost the interconnectivity between the fractures of the well (Gaskari R. and Mohaghegh 2006). The linkage and spacing of the induced fracture networks are still generally not very well understood. Therefore producing companies are grandly motivated to enhance their understanding of these characteristics and this for two reasons: (1) to get more reliable and accurate production and reserve estimates, and (2) to ameliorate their interpretation of the available data in order to enhance their field development strategies and drill, economically, more fruitful wells (M. Y. Soliman, Johan Daal and al. 2012).

In general, shale gas plays present various challenges to analysis that conventional reservoirs simply do not imply. Their very low permeability makes conventional production almost impossible; thus every well in a shale play must be hydraulically fractured to achieve economical production (Holditch S. A. 2006; Sunjay 2012).

As for the techniques applied to estimate potential recoverable reserves contained in underground shale reservoirs, Production Decline Curve Analysis (PDCA) was seen as the most successful characterization technique because it is practical, reliable, and relatively costless (Poston 2005). PDCA is a traditional graphical procedure that monitors and predicts gas production decline rates over time. It is relatively costless because, once compared to other characterization techniques, (1) it is mainly based on extrapolation techniques (a simple assessment of past performance production data of pre-established wells), and so (2) previous production decline trends will be projected in order to predict future potential behavior of newly discovered wells.

Among various methods that can be used within the industry to estimate Gas In Place1 (GIP) in a particular shale formation, those advanced by Arps (1945) and Fetkovich (1987) are thought to be the most popular ones2. They were originally designed to forecast and predict production capacities of conventional reservoirs and vertical wells but once applied solely to the estimation of unconventional gas reservoirs they encounter major key issues and provide unreliable results (Lisa Dean P. Geol. and Eng 2008). Since 2006, shale gas reservoirs started to be widely explored and developed in North America, notably in the U.S., with advances in technology, such as multi-fractured horizontal wells and directional drilling, being the key drivers for success.

1The estimated gross quantity of gas contained within every shale gas play or reservoir. 2The first successful horizontal drilling act that contributed the most to the launching of the North American Shale Gas Revolution was driven by a small private company, Mitchell Energy; it took place in the North Texas Barnett Shale region, and it dates from 1991 (Wikipedia).

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Nowadays, shale plays that have been exploited had numerous and complex reservoir and production characteristics that rendered the mathematical estimation of gas produced from horizontal wells deceptive because it often leaded to unreasonable production estimates (Fekete associates Inc. 2004). The characterization process of shale plays in terms of future production capabilities is divided into two basic elements: (1) the evaluation of the reservoir technical properties (permeability, GIP, etc.) and, (2) the prediction of future production trends of newly discovered wells, being a crucial part of the characterization process, enabling producing companies to estimate existing volumes of Technically Recoverable Resources3 (TRR) in shale plays and eventually to assess the economic profitability of every well drilled (within that play). To this end, this project proposes a base case economic model that facilitates the tasks of evaluating and assessing the profitability of shale gas investments.

Essentially, the assessment of shale plays economics goes through two key stages: the technical (estimating gas reserves) and the economic (William M. Gray, Troy A. Hoefer and al. 2007). From this perspective, our study combines theory modeling and empirical testing in an application field that is novel to the energy economics literature.

The first chapter of our project is entitled: “Shale Gas and Production Decline Curve Analysis”. At first, it will be question to present a brief literature review of the economics of extracting natural gas from shale followed by an attempt to advance answers to three basic questions: (1) What is shale gas? (2) How shale gas is produced? (3) How shale gas reserves are estimated by operators using PDCA?

Later on, comes the second chapter of our project, through which, by analyzing various production decline scenarios of some major shale plays in the U.S. and Canada (Marcellus, Haynesville, Barnett, Montney, and Utica), we identify the economic thresholds that will render shale gas projects economically productive and will hence allow us to detect the economic parameters that impact the productivity of a shale gas reservoir. Our second chapter is entitled “The Economics of Shale Gas Wells”. The latter offers a microeconomic insight into PDCA and the analysis is done following a 2-step methodology: (1) Cash flow analysis (to assess the economic feasibility of the project), and (2) Sensitivity analysis (to monitor how the economics of the project will vary under various cost-production development scenarios). To note that the results found will be stated in terms of NPV (Net Present Value), IRR (Internal Rate of Return) and Payout Periods.

Additionally, since the Utica shale is still in its early stages of development, a simple logistic model that describes the relationship between volumes of gas produced, royalties, and wells deployment time on the scale of the industry will also be proposed. The latter will depict how annual royalties collected (by the government) are positively correlated with continuous drilling activities and how these royalties will tend to dramatically fall post-deployment time.

3The volume of gas which is recoverable using available exploitation and production technology without regard to cost, which is a fraction of the estimated GIP.

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Literature Review

How gas reserves are estimated from unconventional reservoirs plays a central role in assessing the economic feasibility of shale plays (Larry Lake, John Martin and al. 2012). Five years ago, an increasing number of authors, with or without academic affiliations, started to investigate and to evaluate the economic profitability of shale plays (Andrew Potter, Helen Chan and al. 2008; Al-Reshedan 2009; Jeff Ventura, Aubrey k. McClendon and al. 2009; Bailey 2010; Kaiser 2010; Lin 2010; Jason Baihly, Raphael Altman and al. 2011; Kaiser 2012; Larry Lake, John Martin and al. 2012; Mason 2012). However, the assessment of shale plays economics is volatile and the calculated results depend upon the reliability of the assumptions made (in terms of gas price, drilling and completion costs, etc.) before the launch of the analysis. Whilst some assumptions are common to all shale plays, some others are specific to every shale play. The latter varies based on several criteria’s, such as: geographical locations, reservoirs physical properties, proximities to market hubs, etc. In our project, we outline, for every shale play, a specific set of assumptions (Chapter II) and we make a logical interference into the existing literature on shale gas economics by proposing a flexible economic model that fits all types of shale plays.

Part of the theory developed in this project relies on financial analysis formulas that appraise the impacts of the model input-parameters (economic and geologic) variations on the expected financial outputs of the investment. This methodology has been used, among others, by Lin (2010), Bailey (2010), and Kaiser (2012) to test the economics of the Utica, the Barnett and the Hayneville plays, respectively. The typical focus of this literature is to test the economic feasibility of every shale play under different development and market scenarios. In contrast, our study uses cash flow sensitivity analysis to forecast the total stream of financial earnings that could derive from the development of every shale play as well as to study how these anticipated financial outcomes could, over time, increase or decrease based on prevailing economic conditions. Moreover, we introduce a logistic growth model that assesses the impact of wells deployment on both volumes of gas produced and annual royalties paid within the industry in the Utica shale.

Finally, the evaluation of the economic profitability of shale plays is related to a stream of technical work that deals with the estimation of future gas reserves from unconventional formations (Lisa Dean P. Geol. and Eng 2008; Liu Wendy 2008; Jason Baihly, Raphael Altman and al. 2011). The existing literature on shale gas economics is limited in a several number of ways. Mainly, it has not evaluated the economic profitability of producing shale gas while taking into account the fact that it is of a central importance to understand how volumes of gas, that will be in later years, produced and sold, are estimated in the first place, nor has it presented a clear economic explanation of the methods and technical variables that are used within the industry to estimate gas reserves from unconventional formations.

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Chapter I: Shale Gas and Production Decline Curve Analysis

Normally, shale gas plays contain both free (contained within the natural fractures of shale) and adsorbed gas (accumulated on a solid material such as the organic particles in a shale reservoir)4. The latter is rarely commercially produced however the former is the major contributor to economic production. Gas production from shale gas wells is often estimated using traditional decline curves (PDCA) developed by Arps in 1945 and is mainly characterized by high initial production rates, steep decline rates and long term steady low production rates, thereafter.

1.1. Definition: What is Shale Gas?

Shale gas is natural gas trapped in an organic-rich, fine-grained underground rock called shale (González 2012). Shale gas is found in shale formations. It is produced from the fractures and micropores spaces of shales. By shales we mean those underground sedimentary rocks composed of clay and fragments of other minerals such as quartz and calcite (SCGNC 2006). Shale gas is normally generated during underground burial, when heat and pressure crack the organic accumulations. During the process of generation, some of the oil-gas, with high permeability, succeeds to flow and migrate to less deep wellbores (relatively close to the surface), forming the so-called conventional reservoirs, while some other, shale gas (with low permeability), for example, do not succeed to escape the organic matter and still trapped within the shale formation. The latter is the so-called unconventional reservoirs (Holditch S. A. 2006).

Figure 1: Conventional, tight, and shale gas and oil. Adapted from EIA (2011) and Kaiser (2012). Hence, given that typical shale reservoirs are buried few kilometers deep in the ground and are largely distributed over extensive geographic zones rather than concentrated in specific locations, gas shales are usually known as resource plays or reservoirs (Larry Lake, John Martin and al. 2012).

4Source: Schlumberger Oilfield Glossary.

Land_surface

Conventional_non_associated_gas Coalbed_methaneConventional_associated_gas

SealConventional_oil

SandstoneTight_sand_gas

Tight_sand_oil

Oil-rich_shale Gas-rich_shale

Drilling_rigs

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The NEB Report (2009) states that the volume of natural gas, contained within every shale play depends of the thickness and geographic extent of the reservoir. Thus, volumes of GIP increase -the thicker is the reservoir- as the geographic extent of the reservoir grows.

Finally, low permeability indicates the restricted capacity for shale gas to flow easily through shale formations, the reason why, usually, unconventional reservoirs development require more complex stimulation techniques to be economically produced than is the case with conventional reservoirs (T.A. Blasingame 2008).

1.2. How Shale Gas is produced? As noted earlier, shale gas will not easily migrate to any vertical well drilled through it because of the low permeability of shales. Fortunately, recent advances in technology succeeded to solve this problem (Jason Baihly, Raphael Altman and al. 2011). Every decision concerning the eventual commercial development of shale gas requires, ex ante, several years of exploration, collection of data and trials. The different stages that are linked to the exploration activities require the existence of an entity (e.g. producing firm) that is ready (financially capable) to offer whatever huge, but necessary funding without having any guarantee that the project will finally succeed (KPMG Global Energy Institute 2011). Every entity proceeds to the development of shale gas according to its own methodologies and beliefs but, in general, the process goes through five different stages of exploration and evaluation before it comes to the stage of commercial development. Each one of these stages consists in collecting technical information that, once analyzed and executed, will enable the producing firm to pass to the next stage of the producing process. Since the majority of unconventional oil-gas plays are seen to be of low permeability, their production process, once taking place, will require the adoption of specific methods to increase the surface of the reservoir, in liaison with the well. As already pointed, two methods are being currently used: (1) horizontal drilling, and (2) (multi-stage) hydraulic fracturing (fracking).

1.2.1. Horizontal Drilling Firstly, the drilling has to be vertical. The depth of the vertical well is proportional to the depth of the underground location of the shale formation. The former has to stay above the latter. The issue of -low permeability gas production- being uneconomic is now offset by drilling horizontal wells, where the drill bit (cutting tool) is directed from its free fall trajectory to follow a more horizontal path (upon an increasing curvature) for one to two kilometers (can go to 2.5 km), thereby connecting the wellbore to as much reservoir as possible (SCGNC 2006; M. Y. Soliman, Johan Daal and al. 2012).

The horizontal drilling enhances the likelihood of the wellbore to intersect with a much great number of naturally existing fractures in the reservoir. The trajectory of the drill path changes with the changing of the fracture trends in every zone. The arbitration between drilling horizontally or vertically is enhanced access to the reservoir (increases the possibility of recovering more gas); however this is surely done at a way larger cost. Lee (2011) points that drilling is challenging since drilling costs typically comprise half of

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the cost of the wells and access to the reservoir is improved with horizontal drilling which may access a longer productive zone within the reservoir than vertical wells, which the author qualifies as cheap.

1.2.2. Hydraulic Fracturing Hydraulic fracturing techniques commonly known as “fracking” techniques are often used by oil and gas industries to improve low permeability reservoirs (SCGNC 2006). Fluid (often water, sand, proppants and chemicals) is pumped down the well until the pressure exceeds the rock strength and forces the reservoir to crack (induced fractures).

Figure 2: Stages of a shale gas production process. Adapted from (NEB report, 2010). The fracking fluid injected in the wellbore stimulates and helps to maintain the fractures open, which are at the risk of closing again once induced pressure is diminished. There are two main factors that may improve the ability of shale to fracture. The first one is the presence of hard minerals (silica, calcite, etc.), which have grand capacities to induce large fractures in the underground shale as well as to maintain the already existing natural fractures open. However, the second one depends on shale’s internal pressure (Holditch S. A. 2006).

Because of the low permeability of shales, much of the gas cannot escape during the process of generation and builds up an over-pressure inside the rock itself. Therefore, the induced fracture connections can go deeper into the formation because the shale is already closer to the breaking point than in normally pressured shales. The Montney and Utica shales are both considered to be over-pressured (Kim Page and Dave Hammond 2008). Moreover, by creating isolated areas all along the horizontal section of the well, segments of the borehole can be fracked, one at a time, by using a

Gas flows out of well Natural gas is piped to market

(feet)1,000 Recovered water is taken to a treatment plant

Well2,000 Sands keep fissure open Well

3,000 Fissure

4,000

5,000 Mixture of water, sand, and chemicals agents

> 6,000 Well turns horizontal Fractured shaleFissures

A pumper truck injectingwater, sand,and chemicals

into the well

Water trucks forthe fracturing process

Storage tanks

Pit

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technique called multi-stage fracturing. Finally, shales can be re-fracked, over and over, years later, after production has declined, and this, in order to levy, as much as possible, the Recovery Factor5 (RF) of GIP.

This technique allows the well to access more of the reservoir that may have been missed during the initial hydraulic fracturing or to reopen fractures that may have closed due to the decrease in pressure as the reservoir is gradually drawn off of water. Even with hydraulic fracturing, wells drilled into low permeability reservoirs have difficulty communicating far into the formation, therefore, more wells must be drilled (creating pools) to access as much gas as possible (reducing the gap between the GIP and the Gas Produced, GP), normally four, but up to ten, horizontal wells per section (one square mile). Loosely, in conventional reservoirs, the RF of natural gas can reach as much as 85% of the GIP (KPMG Global Energy Institute 2011). However, in unconventional reservoirs in general and in shale gas reservoirs in particular, the RF is typically expected to be nothing more than 20% of the GIP because of its low permeability.

Cost wise, a horizontal well in the Montney shale will approximately cost 5 to 7 million dollars (Dan Magyar and Colin Jordan 2009). However, in the Horn River Basin, as of 2009, a horizontal well costs up to 8 million dollars6. Horizontal wells in the Utica Shale are expected to cost 4 to 7 million dollars (Lee 2010). Vertical wells targeting conventional shale gas, like in the Antrim Shale (Michigan, U.S.), are way cheaper; the resource is shallow, and wells drilled cost less than $250,000 each7.

1.3. Understanding Production Decline Curve Analysis

Production decline curve analysis is one of the most commonly used tools in reservoir and petroleum engineering for the analysis of production data (Adam Micheal Lewis 2007). Usually production rates versus time data are matched to a theoretical model. Future production rates, GIP, and the time of economic limit of a production well can all be predicted based on this history match. It is also possible that an estimation of future economic profits of those wells can be done using this forecast. This section of Chapter I provide an explanation of how gas reserves are estimated within the industry and how PDCA will be useful to us in the fact that it will allow us to determine and to assess the economic feasibility of producing shale gas.

1.3.1. History of Reserves Estimates Calculation Methods Gas is accumulated in limited quantities within the earth (Patzek 2008). It was from the basic understanding of this simple sentence that the earliest attempts to estimate ultimate recovery reserves began.

The first PDCA plot was drawn by Lombardi in 1915. The decline curve represented the production rate behavior versus time, of a large oil field reservoir in California. The second early attempt, in what may concern PDCA, was initiated by Requa, also in 1915, to show the decline percentages for various oil

5The ratio of recoverable gas reserves to the GIP in a shale gas reservoir. 6Maguire V. “The Horn River shale play - Why it works”, 4th B.C. Unconventional Gas Conference, April 2010. 7www.marcellusshales.com/shaleplays.html

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fields in California. Later in the early 20th century, another but more complicated version of the PDCA methodology was advanced by Lewis and Beal in 1918 (Robert C. Hartman, Pat Lasswell and al. 2008). They proposed a more advanced method (production rate behavior versus cumulative production rate versus time) that incorporates the uncertainty relying behind the use of the production decline method, and this was done by the adoption of a probabilistic estimate that is able to generate a wide range of potential outcomes rather than to focus on a single result8.

Johnson and Bollens (1927) were the first to advance a method for calculating future production based on observation. It was from their equation that the form of PDCA used today was born. Arps observed that when the ratio of production rate over change in production rate was constant, the curve plotted as a straight line on a semilog paper, and declined exponentially. Out of this observation came out the most widely used method for estimating gas reserves (Lee 2010):

𝑞� = 𝑞𝑖 × exp (−𝑡𝑎

) (1.1)

Where: 𝑎 = exponential decline constant. 𝑞� = is a constant and denotes the initial production rate in year 0.

Equation (1.1) is referred to as exponential growth or decay. Using the Arps methodology, once it is assumed that a gas well continues to behave today in the same manner as it used to behave yesterday then, the model can easily be applied to forecast the total production of the well and when represented on a semilog graph, the exponential model takes the form of a straight line (Arps 1944). However, in some cases, the Arps plot curvature did not follow a straight line trajectory on a semilog paper, but instead the decline path changed over time at a constant rate. This is most commonly the case of wells with hyperbolic nature (where well’s production data concaves upward)9 and Arps formulated a new mathematical equation that fits this particular attitude of some wells:

𝑞� = 𝑞𝑖 (1 + 𝑏𝐷�𝑡)��/� (1.2)

𝐷� = constant and denotes the initial decline rate, 1/𝑡𝑖𝑚𝑒 at 𝑡 = 0. 𝑏 = hyperbolic exponent (0 ≤ 𝑏 ≤ 1).

Later on, in the late 20th century, appeared the Fetkovich methodology which is originally nothing but an extension of the Arps methodology (Al-Reshedan 2009). Fetkovich (1980) shows that Arps equation could be related to physics, and thus could have a physical meaning. Fetkovich states that 𝑞� denotes the point at which the well first sees the reservoir boundary rather than the peak point of production (in the case of Arps). More precisely, 𝑞� describes the transition flow10 inside the reservoir and denotes the point where the boundary dominated flow stage begins to be observed when the pressure inside the reservoir starts to decline (Fetkovich 1980; Fekete Associates Inc. 2012).

8For more details on the early attempts at decline curves, see (Clark, 2011) in “Decline curve analysis in unconventional resource plays using logistic growth models”, University of Texas at Austin, August 2011. 9www.petrobjects.com 10(Transition)Flow in a reservoir often goes from a transient flow state to a boundary-dominated flow state.

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To note that, transient decline is only observed in wells with low permeability or during the early life of well production. By transient decline, we mean when the pressure inside the reservoir is not constant or steady yet, and the size of the reservoir has no effect on the well performance. On the contrary, when a boundary dominated flow state occurs, the pressure inside the reservoir declines at a constant rate and the reservoir acts like a tank, the reason why, in the existing literature, the Arps methodology is sometimes referred to as a tank type model. The latter denotes the internal energy of gas which is the primary drive mechanism that moves it towards the surface (free gas).

As gas is produced from the reservoir, the pressure inside the reservoir will tend to decline steeply over time (loss in reservoir pressure is the main cause behind the steep decline in shale gas production) (Adam Micheal Lewis 2007; Y. Cho., O. G. Apaydin and al. 2012). For Fetkovich, the pressure flow 𝑛 of a reservoir can be used to determine the hyperbolic exponent 𝑏 of the Arps methodology and the mathematical relation can be written as follows:

𝑏 = 2𝑛

2𝑛 + 1 (1.3)

Thus, 𝑏 and 𝑛 are positively correlated (𝑛 → ∞, 𝑏 → 1). However, certain production declines will not yield a unique solution to the Arps equation so, when multiple solutions occur, the knowledge of 𝑛 is useful to predict the appropriate 𝑏 value that most fits the situation:

b n Description of drive mechanisms

Undeterminable NA Any well in transient flow stage

0

0 ≤ n ≤ ∞

Single phase liquid, high pressure gas, very poor relative gas permeability, etc.

0.3 Typical solution gas drive wells

0.4 ≤ b ≤ 0.5 Typical gas wells

0.5 Water drive in oil reservoirs

For Fetkovich, there are no 𝑏 values greater than one. This phenomenon will never take place if the Arps equation is used adequately. Or what happens if 𝑏 > 1? Is the Arps PDCA method will still be applicable?

1.3.2. PDCA in unconventional reservoirs PDCA consists on matching past production capacity trends with a model. If it can be assumed that the future behavior of a reservoir will be the same as its past trends, the model could be used to estimate GIP and ultimate gas reserves at some future reservoir abandonment pressure or economic production rate (L. Mattar and R. McNeil 1998). Nowadays, several techniques have been developed to evaluate wells performances in unconventional formations but unfortunately no single methodology has proven to be capable of handling all types of data and reservoirs (Fekete associates Inc. 2004).

Early attempts at PDCA required finding plotting techniques or functions that would linearize the production history of a gas reservoir. Linearization was essential because linear functions are simple to

20

analyze and to manipulate mathematically, so the future production capacity of a well or reservoir could then be extrapolated. By definition, decline curves are plots that describe the relationship between “gas production rate” and “time”, or between “gas production rate” and “cumulative gas production”. In general, decline curves are often illustrated based on the Arps hyperbolic rate-time decline equation (1.3). And, depending on the value of the hyperbolic exponent 𝑏, equation (1.3) can take three different forms, and the decline curve will take three different shapes: linear (exponential), when 𝑏 = 0; hyperbolic (curved), when 0 < 𝑏 < 1 and harmonic (tends to be steadier), when 𝑏 = 1. Refer to appendix (A.1. Decline Curves). To note that the most attractive feature in the Arps equation is that it is easy to set up, to use and to analyze (Adam Micheal Lewis 2007). However, this methodology has its failings and as a result, it sometimes provides inaccurate gas production estimates. Concretely, it overestimates gas reserves contained within low permeability reservoirs. The National Petroleum Council Report on unconventional gas in 2008 defined shale gas reservoirs as any reservoir with permeability less than 0.1 millidarcy11 (md). The Barnett and Bakken shales are two examples of shale reservoirs with an average permeability below 0.1 md (Holditch S. A. 2006).

Why PDCA (Arps) do not fit with unconventional reservoirs? The problem is largely of a mathematic nature. With 𝑏 > 1, Arps’s method overestimates gas reserves and gas cumulative production becomes infinite, however, this is simply unreliable because the amount of hydrocarbons in the ground is finite. Despite its shortcomings, the Arps equation is still largely used within the industry. When used for economic purposes, gas production is truncated at an uneconomic production rate and the results for 𝑏 > 1 are best represented on a semilog plot of gas flow rate versus cumulative production (Figure 3). The existence of 𝑏 > 1 in unconventional reservoirs is mainly due to the extended transient flow regime that characterizes low permeability shale formations. However, the inaccuracies that result when using the Arps hyperbolic decline equation to estimate gas reserves from low permeability formations (𝑏 > 1) were grandly identified and serious efforts have been made to develop new techniques that replace the Arps methodology and correct its shortcomings.

Among others, we limit our curiosity to just two of the methods that were developed, notably: (1) The exponential truncation of hyperbolic equations method, and (2) The multiple transient hyperbolic exponents’ method.

Figure 3: DCA, rate versus cumulative gas production. Adapted from (Fekete associates Inc. 2005).

11A darcy (d) and millidarcy (md) are units of permeability. They are used in petroleum engineering and geology.

0 1 2 3 4 5 6

Gas_

Rate

Cumulative_gas_production

EUR = 5 Bcf

21

1.3.2.1. Exponential truncation of hyperbolic equations Developed by Maley in 1985 (Satinder Purewal, James G. Ross and al. 2011). This method suggests that at some point of the production life cycle of a shale gas reservoir, the hyperbolic decline (0 ≤ 𝑏 ≤ 1) has to switch to an exponential decline (𝑏 = 0). Maley proposes the use of two separate models to implement this methodology. The latter has no physical meaning and its only purpose is to prevent the issues of having explosive solutions in the estimates when using the Arps methodology. Refer to appendix (A.1. Decline Curves).

Furthermore, from an economic point of view, Maley (1985) points that after 15 or more years of gas production from a certain shale play or reservoir, the monetary value of gas produced will tend to have a discounted zero value in today’s dollars. The latter is confirmed by the fact that most producing companies consider the first 10 years of the life cycle of a well to be the most important because the majority of the EUR will be produced during this period and will, eventually, drive the project economics.

1.3.2.2. Multiple transient hyperbolic exponents Spivey and al. were the first ones to suggest using multiple 𝑏 values. They showed that 𝑏 will change over time. During the early stages of production in a tight gas reservoir, the dominant flow regime is a linear flow. This flow regime correspond to 𝑏 = 2. Thus, based on a report launched by Fekete Inc., we can associate 𝑏 = 2 to nothing but an upper limit to the potential volume of gas that can be produced from a shale play. Typical 𝑏 values often range between 0.3 and 0.8 bcf (Lisa Dean P. and Eng 2008).

Under a flow regime, a 𝑏 value of 2 might occur during the early life of the well (transient flow regime). This is mainly the case of gas production trends in the Bakken shale12. However, with time, 𝑏 tends to decrease, and so when a transition into a boundary dominated flow regime occurs, the flow of production data will fit with a 𝑏 value of 0.25. Thus, the typical extreme lower and upper bounds of 𝑏 values are thought to be 0.25 and 2. Finally, if enough production data, concerning wells that were previously drilled in a certain area, is available, the multiple transient hyperbolic exponents method could yield to better and more pragmatic (less arbitrary) results than the exponential truncation method.

Now that we have explained some of the most important technical concepts that characterize the processes of estimating and producing shale gas reserves from unconventional formations, we proceed in our analysis to the assessment of shale plays economics.

12Wikipedia 2012. Bakken formation, http://en.wikipedia.org/wiki/Bakken_formation (visited: 10 November).

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Chapter II - The economics of shale gas wells The understanding of the technical differences that separate the economics of extracting shale (unconventional) gas deposits from those of extracing conventional gas deposits is essential to the pursuit of our analysis. Relatively, shale gas plays are characterized by lower finding risk and higher economic risk (Andrew Potter, Helen Chan and al. 2008). Since the late twenieth century, it is mainly the U.S. experience in terms of producing shale gas that proved the likelihood of this unconventional resource, relatively to other conventional sources of energy (coal, nuclear, etc.), to become the potential game changer for the energy industry worldwide. Figures 4 and 5 show the potential supply trends of six different, conventional and uncoventional, sources of energy between 2006 and 2030 in the U.S. as well as the largest source of U.S. natural gas supply between 1990 and 2030, respectively.

Figure 4: Energy trends in the U.S. (Deo 2007).

Figure 5: Natural gas production by source, 1990-2030 (U.S.). Adapted from (González 2012).

Hence, the vastness of shale formations signifies that there is a little risk associated with finding the hydrocarbon in place (GIP), however, the likelihood of commercial development is highly dependent on the decision to drill pilot wells which is commensurate to a commitment to complete the well (William M. Gray, Troy A. Hoefer and al. 2007).

24

Thus, these wells must be fractured even before the economic viability of the well can be determined. Moreover, given the fact that shale gas production deplete rapidly and the depletion often takes place during the early life of the well, a conventional well might produce 30 to 40 bcf of gas over its life whereas a shale gas well would produce nothing but a fraction of this amount (Larry Lake, John Martin and al. 2012). Those rapid initial decline rates characteristic of unconventional reservoirs are, at some extent, decisive of the economic profitability of shale gas production. Therefore, the ability to understand these variables as well as their respective impacts on the economic feasibility of shale plays is vital to our, next to come, economic analysis.

When production profiles in major gas regions are examined, what we generally see is a rising output that peaks after a certain period of time, and then starts to decline to reach finally its economic limit (to be defined later), even though there may still be a huge amount of the resource remaining in the ground (Banks 2008). As showing in figure 6, after the decline phase, the play reaches its economic limit. At this stage, no further production, nor economic or financial returns can still be expected. The reservoir (play) is said to be out of pressure and no more gas can further be economically produced (Fekete associates Inc. 2005).

Figure 6: Shale Gas Production Economics (Banks 2008)

Kaiser (2010) defines the economic limit of a reservoir as the time when the net revenue (gross revenue net of royalty) of the field is equal to the field production cost (including taxes, operating and transportation costs). To further extend the plateau, it may have a positive effect on the amount of gas that can still be recovered, however, on the basis of reserves that have been recovered in a particular deposit or field, it is uneconomical to attempt to prolonge the plateau indefinitely (Banks 2008).

Commonly, the biggest challenge in a shale gas investment is the capacity of operators to determine the EUR of a shale reservoir. Since decline analysis is relatively simple, it was and will be adopted. Decline curve analysis and EUR predictions are found in the public domain. Also, our analysis will be limited to wells with publicly available data and will not include production improvements from workovers nor recompletions or re-fracks.

Q (t)

Decline

Build-Up

Time

Costs

Economic limit

Clean-up Costs

Plateau

Additional investment

25

The first section of chapter II describes and explains the model, the methodology, and the model economic parameters. The second section presents a comparative assessment of the economic profitability of producing shale gas from five different shale plays (Barnett, Haynesville, Marcellus, Montney, and Utica). Finally, the third section of our chapter introduces a logistic model that computes various wells deployment scenarios within the industry in the Utica shale. Finally, conclusions and recommendations will be advanced based on the results found.

2.1. Model, Methodology, Variables Our study is conducted through decline curve analysis and breakeven economics. More precisely, the profitability of shale gas production will be examined through cash flow sensitivity analysis. The main purpose of evaluating the economics of shale gas projects is to calculate financial revenues that derive from the production and the commercialization of shale gas under multiple development scenarios of the industry. The same methodology is used by Lin (2010), Kaiser (2012), and Larry Lake, John Martin and al. (2012).

2.1.1. Cash flow analysis The importance of applying a cash flow analysis when assessing the economic feasibility of producing shale gas is that it allows us to simulate and to test the impact of technical and financial inputs (to be mentioned throughout the analysis) characteristics of shale gas economics on the anticipated financial returns of the project.

Kaiser (2012) and Lake and al. (2012) point that the economic profitability of shale gas investments should be tested by computing the total stream of after-tax net cash flows generated by the project. The after-tax net cash flow is the difference between the estimated financial profits and the estimated financial charges of the project over t periods, denoting the life span of the project. In our project, we suppose that t = 20 years. Mathematically, the latter can be written as follows:

𝑁𝐶𝐹� = 𝑇𝑁𝑅� – (𝑅𝑂𝑌� + 𝐶𝐴𝑃_𝐸𝑋� + 𝑂𝑃_𝐸𝑋� + 𝐼𝑛𝑐_𝑇𝑎𝑥�) (1.4)

Where:

𝑁𝐶𝐹� denotes the after-tax net cash flow of the project (can be positive or negative), in year t, 𝑇𝑁𝑅� denotes Total Nominal Revenues in year t, 𝑅𝑂𝑌� denotes Royalties paid in year t, 𝐶𝐴𝑃_𝐸𝑋� denotes Capital Expenditures paid in year t, 𝑂𝑃_𝐸𝑋� denotes Operating Expenditures paid in year t, and finally, 𝐼𝑛𝑐_𝑇𝑎𝑥� denotes the corporate income tax rate paid in year t.

2.1.1.1. Explaining the model variables When assessing the economic profitability of an investment project, a cash flow analysis consists on computing the total stream of potential financial outcomes that could potentially be generated once the project is brought on-line. The same logic applies to the assessment of the economics of shale gas production.

26

Total Nominal Revenues in year t, 𝑇𝑁𝑅� denotes the potential financial profits that derive from the launching of a shale gas project. The latter will be equal to the natural gas price 𝑝 paid in year t multiplied by the volume of gas produced 𝑞� during the same year.

𝑇𝑁𝑅� = 𝑝 × 𝑞� (1.5) To note that 𝑞� is estimated using Arps hyperbolic equation (1.2). Thus, if we replace 𝑞� by its value in equation (1.5), we obtain:

𝑇𝑁𝑅� = 𝑝 × �𝑞� × (1 + 𝑏𝐷�𝑡)���� (1.6)

Where 𝑝 and 𝑞� are two constants denoting natural gas price and initial production rate, respectively

and (1 + 𝑏𝐷�𝑡)��� is a function of time that we represent as 𝑓(𝑡). As a result, equation (1.6) can be

rewritten as:

𝑇𝑁𝑅� = (𝑝 × 𝑞�) × 𝑓(𝑡) = 𝛾 × 𝑓(𝑡) (1.7) Equation (1.7) shows that if 𝑝 increases by two points (all other variables held constant), so will do 𝑇𝑁𝑅�. Any variation of 𝛾 implies that 𝑇𝑁𝑅� and 𝑞� will vary proportionally (linearly) over time. The value of 𝛾 will depend upon two factors: (1) the economic environment under which firms choose to operate and (2) the geologic properties of shale plays. To mention that we compute 𝑝 using the publically available Henry Hub13 average prices forecasts. These forecasts show that average natural gas prices will range between 2 and 8 dollars per mcf between 1990 and 2030.

As for Royalties 𝑅𝑂𝑌� . It represents a prospective cost to producing companies, generally a variable fraction 𝜃 or financial charge to be paid to the government or to the land owner, per unit of production. Mathematically, 𝑅𝑂𝑌� can be written as:

𝑅𝑂𝑌� = 𝜃 × 𝑞� (1.8)

Royalties can be found in the public domain and differ from a country (region) to another. Royalties in the U.S. and Canada vary between 15 and 35% relatively to the amount of gas produced and sold (Andrew Potter, Helen Chan and al. 2008). In Quebec (in the case of the Utica shale), the yearly fraction of royalties that is to be paid to the government is largely dependent on annual volumes of gas produced and prevailing natural gas prices. Quebec’s new royalty regime defines a range of 5-35% for royalty rates.

The larger the volume of gas produced (the higher the price of gas) , the bigger the fraction of royalties that will be paid and vice versa (Ministères des Finances 2011). Royalty rates specific to every shale play will be defined later.

13The Henry hub is a distribution hub on the natural gas pipeline system in Erath, Louisiana, owned by Sabine Pipe Line LLC. The pricing is based on natural gas futures contracts traded on the New York Mercantile Exchange (NYMEX). Ref.: Wikipedia.com.

27

Lake and al. (2012) points that in a shale gas project, drilling forms 60% of the total costs of producing shale gas and completion forms the remainder 40%. Kaiser (2012) states that capital expenditures consist of land acquisition, drilling and completion costs, pipeline infrastructure, etc. Kaiser (2012) also mentions that those costs are the main costs in a shale gas production project. Hence, in our project, capital expenditures, 𝐶𝐴𝑃_𝐸𝑋� will only consist of drilling and completion costs. In the analysis, we suppose that 𝐶𝐴𝑃_𝐸𝑋� is a fixed cost or an initial investment cost that will be paid once at the launch of the project (𝑡 = 0, 𝐶𝐴𝑃_𝐸𝑋� = 𝐶𝐴𝑃_𝐸𝑋). Capital expenditures specific to every shale play will be specified later.

Operating expenditures or more precisely Lease Operating Expenditures (LOE) are defined as being the costs that are associated with work physically performed at the work site (Kaiser 2012). In our project, for simplicity purposes, we do not distinguish between the production of dry(cheaper)-wet(more expensive) gas and we consider 𝑂𝑃_𝐸𝑋� as being a yearly (variable) cost, per unit of production. Finally, the corporate income tax rate or simply the Income tax rate 𝐼𝑛𝑐_𝑇𝑎𝑥� is a yearly amount of money (a fraction 𝜑 of 𝑁𝐶𝐹�) that is paid to the government once the production process of the resource has started. Mathematically, 𝐼𝑛𝑐_𝑇𝑎𝑥� is computed as follows:

𝐼𝑛𝑐_𝑇𝑎𝑥� = 𝜑𝑁𝐶𝐹� (1.9) In our project, we suppose an average taxation rate of 25% (Utica and Montney) (Lin 2010) and a range of 30-50% (U.S. plays) (Kaiser 2012). In our analysis, we intentionally ignore some other types of costs, such as: intangible costs, allowances, depletions costs, and we assume that those costs are directly included in the initial investment cost (𝐶𝐴𝑃_𝐸𝑋) the reason why capital expenditures specific to every shale play will be partially majorated in order to include those costs.

2.1.2. Sensitivity analysis In most cases, in addition to the cash flow analysis, a sensitivity analysis of the project economics is necessary to examine how the uncertainty in the model output can be allocated to various sources of uncertainty in the model input (and vice versa). In our model, we define a base case development scenario (average scenario, P50) for every shale play from which we launch our sensitivity analysis by introducing an expected range for every shale gas input parameter. Thus, the average scenario (in terms of production performance) will, at a certain extent, form the median of the expected range defined. We also introduce two extreme case scenarios, an optimistic one (high development scenario, P10) and a pessimistic one (low development scenario, P90), for every shale play, which are certainly less likely to happen. In our study, we only use this nomenclature to categorize and represent well’s production performances in terms of IP rate, Di rate and EUR.

More precisely, we define a set of P10, P50, and P90 scenarios for every shale play tested in our model to represent wells with the best, average, and worst production performances, respectively.

This measure will allow us to define an upper and a lower bound for the calculated Net Present Value (NPV) in every case (see, decision making indicators), allowing us eventually to compare the breakeven economics of every shale play tested in our model.

28

To note that the sensitivity analysis will be applied to all three types of wells in every shale play. P10 profiles (wells) will obviously lead the most favorable economics and P90 the least favorable and the results differential found will enable us to define profitability windows specific to every shale play. The input ranges defined will vary from a shale play to another. Larger expected parameters ranges (inputs) will be associated to larger amounts of uncertainty in the results (outputs) found.

Finally, the sensitivity analysis allows us to test the robustness of the results obtained in the cash flow analysis. The sensitivity analysis input parameter combinations used are mainly three: (1) Gas Price and IP rate (2) Gas Price and CAP_EX, and (3) Gas Price and CAP_EX to test the impact of -First Year Gas Price (FYGP)- on P50 NPV project economics. The rest of the input parameters will be considered as static over time. The outputs found in every case will be represented in Matrix-Tables and will be stated in terms of NPV ($million), IRR (%), and Payout Period (years).

2.1.3. Decision making parameters

The economic indicators that will serve as decision making tools are three: (1) the NPV, (2) the IRR, and (3) the payout period (or the economic limit of every shale play).

2.1.3.1. Net Present Value

The 𝑁𝑃𝑉 is the after-tax net stream of discounted cash flows (𝐷𝐶𝐹�) generated by the project. It uses the time value of money to evaluate long term projects. It computes the excess or shortfall of cash flows, in present value terms, once financial charges are met. Thus, it can serve as an investment decision making tool. Generally, the investment options of a prudent company are three: Growth (Go), Shutdown (No-Go) or temporary abandonment (conditional), respectively if the NPV is positive, negative, or nil. Mathematically, the NPV can be represented as:

𝑁𝑃𝑉 = � 𝐷𝐶𝐹��

��� (1.10)

� 𝐷𝐶𝐹��

���= �

𝑁𝐶𝐹�(1 + 𝑟)�

�

���

(1.11)

And so, from equations (1.4), (1.10), and (1.11), we can write:

𝑁𝑃𝑉 = −𝐶𝐴𝑃_𝐸𝑋 + ��[𝑇𝑁𝑅� − (𝑅𝑂𝑌� + 𝑂𝑃_𝐸𝑋� + 𝐼𝑛𝑐_𝑇𝑎𝑥�)

�

���

] ×1

(1 + 𝑟)�� (1.12)

After rearranging equation (1.12) and replacing every variable by its expression, the mathematical formula for the NPV can finally be represented as follows:

𝑁𝑃𝑉 = (1 − 𝜑) �� [𝑝 − (𝑐 + 𝜃(𝑞�))] × 𝑞�

(1 + 𝑟)�

�

���

− 𝐶𝐴𝑃_𝐸𝑋� (1.13)

29

Where:

NPV denotes the after-tax net present value, (1 − 𝜑) denotes the 𝐼𝑛𝑐_𝑇𝑎𝑥� rate to be paid in year 𝑡 as a fraction of the before-tax NPV generated in the same year; the latter is equal to the term showing in the

second parenthesis {…} on the right side of the equation, �(���)� denotes the discount factor (𝑟 denotes

the interest rate), [𝑝 − (𝑐 + 𝜃𝑞�)] × 𝑞� denotes the marginal profit of producing (𝑋 + 1) mcf of shale gas, and (𝑐 + 𝜃𝑞�) computes all the variable costs (including operational costs, royalties, etc. as a function of 𝑞�) that the shale gas production process may imply.

Moreover, our analysis supposes one additional assumption stating that income taxes will only be paid if 𝑉 is positive. 𝑉 denotes the before-tax NPV. This assumption implies that the after-tax NPV will potentially have two values depending on whether 𝑉 is positive or strictly negative:

𝑁𝑃𝑉 = �

(1 − 𝜑)𝑉 if 𝑉 ≥ 0 𝑎𝑛𝑑

𝑉 if 𝑉 < 0� (1.14)

By developing and simplifying some of our model formulas, we made the correlational relationship between our model input (Gas Price, IP rate and CAP_EX) and output parameters (NPV) clearer to see. To note that the results of our NPV sensitivity analysis are all calculated based on both equations (1.13) and (1.14).

2.1.3.2. Internal Rate of Return (IRR) The 𝐼𝑅𝑅 is often used in capital budgeting and can be defined as the discount rate for which the 𝑁𝑃𝑉 = 0. Mathematically, the IRR is the annualised effective discount rate required for the NPV of a stream of cash flows to equal zero therefore, equation (1.11) can be rewritten as follow:

𝑁𝑃𝑉 = �

𝑁𝐶𝐹�(1 + 𝐼𝑅𝑅)�

�

���

= 0 (1.15)

Knowing that the 𝐼𝑅𝑅 is not affected by the 𝐶𝑜𝐶 (Cost of Capital) and the 𝐶𝑜𝐶 is a benchmark against which the 𝐼𝑅𝑅 can be evaluated, comparing the 𝐼𝑅𝑅 to the 𝐶𝑜𝐶 should only be made when making investment decisions. The calculated 𝐼𝑅𝑅 denotes the maximal acceptable value of 𝐶𝑜𝐶 for the project's 𝑁𝑃𝑉 to be profitable. If the 𝐼𝑅𝑅 > 𝐶𝑜𝐶 the project is said to be profitable (𝑁𝑃𝑉 > 0). However, if the 𝐼𝑅𝑅 < 𝐶𝑜𝐶 (𝑁𝑃𝑉 < 0), the project should not be undertaken. So, whilst a higher 𝐶𝑜𝐶 has zero impact on the 𝐼𝑅𝑅, investment decisions will be rarely seen as profitable when using the 𝐼𝑅𝑅 as an indicator of assessing those investment decisions.In our project, for simplify reasons, we suppose that a single firm (Y) is exploiting all shale plays subject of our study and we assume that its 𝐶𝑜𝐶 is about 10% (relatively to its debts and equities)14.

14Brealey R., Myers S. & Marcus A., Fundamentals of Corporate Finance, 3rd Edition, McGraw-Hill, 2001.

30

In conclusion, investment decisions based on the calculated 𝐼𝑅𝑅 will depend upon the cost of capital and the corporate objective of the firm as well as on its financial situation (financial exposure, solvability & debt to equity ratios, etc.).

2.1.3.3. Payout Period On an after-tax basis, payback period is simply the time 𝑇 required by the producing company to recover all the prepaid financial charges that are associated with the project, mainly royalties and drilling and completion costs (Kaiser 2010). However 𝑇 is uncertain and varies based on market and economic conditions. Thus, it’s positively correlated with increases in gas prices and production levels, and vice versa. So, while taking into account market and economic conditions, payback or payout periods denote the earliest time required 𝑚 for the cumulative cash flow to recover well costs.

Mathematically, the payout formula can be written as follows:

𝑇 (years) = �� 𝐷𝐶𝐹� =�

���0� (1.16)

where 𝐷𝐶𝐹� denotes the vector of net cash flows in year 𝑡 [(𝑡 = 1, … , 20) 𝑦𝑒𝑎𝑟𝑠].

2.2. Shale plays and model assumptions In this part of chapter II, a brief description of every shale play subject of our study will be proposed. We also define shale plays model input parameters and their expected ranges. Our choice of the inputs and their expected ranges will be justified throughout the analysis. The expected performances of every shale play are summarized in Tables 1 (a, b, c, d, and e). The latter are collected from the public domain and from various other academic sources.15

Tables 2 (a, b, c, d, and e) summarize the input parameters of shale plays and their expected ranges. To add that our analyis is simply built on a after-tax basis and doesn’t assess the impact of income taxes on the economic feasibility of shale plays and its only aim is to assess the economic feasibility of producing shale gas from various shale plays under multiple development scenarios.

15Engelder 2007; Andrew Potter, Helen Chan and al. 2008; Dan Magyar and Colin Jordan 2009; Jeff Ventura, Aubrey k. McClendon and al. 2009; Bailey 2010; Lin 2010; Kaiser 2012; Larry Lake, John Martin and al. 2012; Mason 2012.

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2.2.1. Barnett

The Barnett shale is a geological shale formation located in the Forth Worth Basin, Texas, U.S. The formation is known as a tight gas reservoir indicating that the gas is buried almost 7000 feet deep and cannot be easily extracted. Its estimated geographical extent is about 5000 square miles. The first attempts of producing shale gas from the Barnett formation date from 1981. However, the effective production of shale gas took place in 1999. This particular shale formation has been considered to have significant underground gas reserves with almost 44 tcf of TRR and 327 tcf of GIP (González P. and al. 2012). Tables 1.a and 2.a present the expected performances for Barnett wells and its shale gas model parameters and their expected ranges, respectively.

In Table 1.a, we define a set of initial production rates for Barnett wells based on three production performance scenarios. We assume that P10 wells will have an IP rate of 5 Mmcf/day, P50 wells will have a 3.5 Mmcf/day IP rate, and finally P90 wells will have an IP rate of 2 Mmcf/day. Our set of IP rates is somehow justified by the fact that a typical Barnett well will have an IP rate of approximately 3.5 Mmcf/day (Jeff Ventura, Aubrey k. McClendon and al. 2009).

And so, P10 and P90 wells are basically set by defining a standard deviation of about ∓1.5 relatively to P50 wells (median). The first year decline rate for Barnett wells is assumed to be the same for all well performances and is set to 72%. The latter is merely higher than the decline rate used in Bailey (2010) (66%) and merely lower than the one used by Ventura (2009) (73%). Decline rates for the rest of the years are calculated using Arps equations. We also set a conservative range of EUR for every production scenario.

Variable Code Unit P90 P50 P10

Initial production rate IP_rate Mmcf/d 2 3.5 5

Initial Decline rate ID_rate % per year 72 72 72

Estimated Ultimate Recovery EUR bcf per year 1.5 2 2.5

Table 2.aLow Average High

Capital expenditures CAP_EX $million 4.5 3.5 2.5

Operational expenditures OP_EX $/mcf 1.5 1.25 1

Royalty rate Disc_rate % per year 25 25 25

Gas price GP $/mcf 2 5 8

Discount rate Disc_rate % per year 10 10 10

Corporate tax rate Inc_Tax % per year 30 30 30

Table 1.a Wells production performance

Barnett (Texas, U.S)

Development scenarios

32

Often, typical Barnett wells have an EUR of 2.5 bcf per year (Jeff Ventura, Aubrey k. McClendon and al. 2009). However, in our project we suppose that P10 wells will have an EUR of 2.5 bcf/year and P50 and P90 wells will have 2 and 1.5 bcf/year of EUR, respectively. In Table 2.a we set a range for every input parameter that characterizes the potential development scenario of the industry. We suppose that 𝐶𝐴𝑃_𝐸𝑋 will range between [2.5, 4.5] in million of dollars (2.5 is the minimum value that 𝐶𝐴𝑃_𝐸𝑋 can take and 4.5 is its maximum possible value).

The average 𝐶𝐴𝑃_𝐸𝑋 scenario (3.5 million$) is set only to be used in the (𝐺𝑎𝑠 𝑃𝑟𝑖𝑐𝑒, 𝐼𝑃 𝑟𝑎𝑡𝑒) sensitivity analysis. 𝐶𝐴𝑃_𝐸𝑋 is assumed to be the lowest under the high development scenario (P10) because it is representative of the long run supply curve (growth) of the industry as a whole under which production average costs will tend to decrease over time (know-how, advances in technology, economies of scale, etc.) as long as the general level of gas produced and the marginal productivity of capital are increased. This assumption is mainly representative of both external economies16 (positive externalities) and economies of scale (cost advantages) long run concepts in the economic theory where factors of production (capital, technology) will increasingly be incorporated into the production process leading to higher production levels and eventually to lesser costs per unit produced. This particular assumption applies to all shale plays subject of our study. To note that in our project 𝐶𝐴𝑃_𝐸𝑋 scenarios are majorated to include some other costs such as: intangible costs, depreciation, etc. Ventura (2009) points that capital expenditures for typical Barnett wells are about 2.3 million$ (horizontal wells only). We also define a range of operating expenditures for the Barnett play of [1, 1.5] dollar per mcf of gas produced.

A similar range of 𝑂𝑃_𝐸𝑋� for the Barnett play can be found in (Bailey 2010), (Andrew Potter, Helen Chan and al. 2008) and (Jeff Ventura, Aubrey k. McClendon and al. 2009). We also assume that the royalty rate for the Barnett play is 25% on an annual basis17. Finally, the tax on income was found in the public domain and was somehow randomly set. For the Barnett play, we assume that the 𝐼𝑛𝑐_𝑇𝑎𝑥� is about 30% per year.

16Bourguinat Henri. Economies et déséconomies externes. In: Revue économique. Volume 15, n°4, 1964. pp. 503-532. http://www.persee.fr/web/revues/home/prescript/article/reco_0035-2764_1964_num_15_4_407615. 17http://blumtexas.tripod.com/barnettshalegas.html

35

2.2.2. Marcellus

The Marcellus formation is a sedimentary formation located in North Eastern America. It extensively passes throughout the northern Appalachian basin and runs across the states of New York, Pennsylvania, Virginia, Ohio, and Maryland. Its estimated geographical extent is 95000 square meters. Typical Marcellus shale wells have initial production rates of about 4 Mmcf/day and EUR of 4.4 bcf. The estimated TRR in this particular shale formation is 280 tcf and the GIP is estimated to be of about 1500 tcf (Engelder 2007; Jeff Ventura, Aubrey k. McClendon and al. 2009; González P. and al. 2012).

Before 2000, when the drilling started in the Marcellus formation, few experts thought that the Marcellus shale would become a major source of natural gas. At first, wells drilled through it using natural fractures systems produced gas in low quantities. Later on, with advances in technology, Marcellus wells became economically productive and the Marcellus formation is now considered as the giant gas field that will offset the future energy security concerns of the United States. Tables 1.b and 2.b below present the expected performances for Marcellus wells and shale gas model parameters and their expected ranges, respectively.

In Table 1.b, we define a set of initial production rates for Marcellus wells based on three production performance scenarios. We suppose an IP rate of 5 Mmcf/day for wells with highest production performances, an IP rate of 4 Mmcf/day for wells with average production performances, and an IP rate of 3 Mmcf/day for wells with lower production performances. The set of IP rates and EUR that are associated to it can be found in Engelder (2007), Potter (2008) and Ventura (2009). We also assume that the ID rate of typical Marcellus wells is 70% (Potter 2008). The latter applies to all development scenarios.

Variable Code Unit P90 P50 P10

Initial production rate IP_rate Mmcf/d 3 4 5

Initial Decline rate ID_rate % per year 70 70 70

Estimated Ultimate Recovery EUR bcf per year 3.5 4 4.5

Table 2.bLow Average High

Capital expenditures CAP_EX $million 5.5 4 2.5

Operational expenditures OP_EX $/mcf 1.1 1 0.9

Royalty rate Disc_rate % per year 15 15 15

Gas price GP $/mcf 2 5 8

Discount rate Disc_rate % per year 10 10 10

Corporate tax rate Inc_Tax % per year 30 30 30

Table 1.b

Marcellus (U.S)

Wells production performance

Development scenarios

36

In Table 2.b, we set a range for every input parameter that characterizes the potential development scenario of the industry. We suppose that 𝐶𝐴𝑃_𝐸𝑋 will range between [2.5, 5.5] million dollars. Potter (2008) and Ventura (2009) assume that the average cost of drilling a single well in appalachia is almost 4 million$. Thus, in our project, we associate a 4 million$ 𝐶𝐴𝑃_𝐸𝑋 to the average development scenario of the industry and we set a standard deviation of ∓1.5 relateviley to the average scenario in oder to define 𝐶𝐴𝑃_𝐸𝑋 for high and low development scenarios, which are 2.5 and 5.5 million$, respectively. Relatively to the case of the Barnett shale, we set lower operating costs for the Marcellus that range between 0.9 and 1.1 $/mcf. Ventura (2009) sets operating costs in the Marcellus shale to 0.95$/mcf. Generally, royalty rates are lower in appalachia relatively to other US shale plays. The majority of Marcellus lands are freehold, with legislated royalties of 12.5 to 15% (Potter, 2008; Ventura, 2009). In our project we adopt the upper bound royalty rate which is 15%. Finally, for simplicity reasons, we assume that the tax rate on income is the same as it is the case for the Barnett shale (30% per year).

37

2.2.3. Haynesville

The Haynesville formation is a sedimentary formation that underlies thet states of Arkansas, Louisiana and Texas from the south west to the northwest side of the United states. Its estimated geographical extent is almost 9000 square miles. It contains 60 tcf of TRR. It came to scene and knew its boom in 2008. Since that date the Haynesville is seen as a major potential shale gas resource. Recently, some experts have estimated that the Haynesville underground recoverable gas reserves are of the order of 250 tcf, and if true, the Haynesville would be considered as one of the largest natural gas fields in North America (González P. and al. 2012).

Tables 1.c and 2.c showing below present the expected well performances for Haynesville wells and shale gas model parameters and their expected ranges, respectively. Since 2008, the economics of the Haynesville shale was extensively analyzed by several authors with or without economic and/or academic affiliations, such as: Potter (2008), Kaiser (2010), Williams (2008), Kaiser (2012), and Lake and al. (2012). In our project, the majority of Haynesville shale play model input parameters are found in Kaiser (2012) and Lake and al. (2012).

We suppose that Haynesville P10, P50, and P90 wells have IP rates of 14 Mmcf/day, 11 Mmcf/day, and 8 Mmcf/day, respectively. Almost the same set of IP rates range can be found in Kaiser (2012), however, our assumptions can be seen as relatively less optimistic. The same logic applies to the set of EUR that we propose. Ventura (2009) supposes that the initial decline rate for Haynesville wells is 82%. As showing in Table 1.c, we assume a merely higher ID rate of 85% for all types of wells.

In Table 2.c, we assume that capital expenditures for Haynesville wells range between 6 and 10 million dollars. Kaiser (2012) points that capital expenditures for Haynesville wells range between 5 and 15

Variable Code Unit P90 P50 P10

Initial production rate IP_rate Mmcf/d 8 11 14

Initial Decline rate ID_rate % per year 85 85 85

Estimated Ultimate Recovery EUR bcf per year 4.5 5.5 7

Table 2.cLow Average High

Capital expenditures CAP_EX $million 10 8.5 6Operational expenditures OP_EX $/mcf 2 1.5 1

Royalty rate Disc_rate % per year 25 25 25

Gas price GP $/mcf 2 5 8

Discount rate Disc_rate % per year 10 10 10

Corporate tax rate Inc_Tax % per year 45 45 45

Development scenarios

Table 1.c Wells production performance

Haynesville (U.S)

38

million dollars. The latter is justified by the fact that we assume a lower IP rate (14 Mmcf/d) for Haynesville P10 wells than it’s the case in Kaiser (2012) where P10 wells have a 16.1 Mmcf/d IP rate. For operating expenditures, the same logic applies as it’s the case for 𝐶𝐴𝑃_𝐸𝑋, however, we only set a narrower range for 𝑂𝑃_𝐸𝑋� and we assume that those costs will vary between 1 and 2 mcf/$.

We also assume a single, unchanging royalty rate of 25% for all Haynesville wells (Potter 2008; Ventura 2009; Kaiser 2012; Lake and al. 2012). Finally, we suppose a relatively high corporate tax rate of 45% per year in the Louisiana region. The latter was set randomly from the range of corporate tax rates [35-50%] found in Kaiser (2012) and could potentially affect or constraint the calculated Hayneville play NPV project economics which should be understood.

39

2.2.4. Montney

The Montney formation is a 20,000 square feet natural gas field located in British Columbia, Canada, and extends into Alberta (González P. and al. 2012). Natural gas can be found in large quantities trapped in this shale play. Hence, the Montney shale play is seen to be according to a report by investment advisor Raymond James Ltd, “one of the largest economically viable shale gas deposits in North-America”. Moreover, according to the estimates of Halliburton, the Montney shale play probably contains 50 tcf of GIP. Thus, since the early 2000’, a lot of producing companies like Talisman, Encana, Enersight and others showed huge interest in having land leases to start exploring and drilling for shale gas in this particular area.

Tables 1.d and 2.d below present the expected well performances for Montney wells and shale gas model parameters and their expected ranges, respectively.

The ranges of IP rates and EUR that are associated with Montney P10, P50, and P90 wells was somehow arbitraty set and so, the results found should be understood. A 6 Mmcf/day IP rate that is associated to Montney P10 wells was taken from a best-fit production decline curve of Montney wells production performances presented during the Unconventional Gas Confernce, CSUG, 2009. Thus, IP rates and EUR showing in Table 1.d are not perfectly accurate and Montney wells could potentially register higher initial production rates (and eventually have higher EUR) than those performances adopted in our project. Moreover, all the other input parameters showing in both Tables 1.d and 2.d was taken from a conference article of Enersight and BOE solutions presented during the same unconventional gas conference in 2009 (Dan Magyar and Colin Jordan 2009).

Variable Code Unit P90 P50 P10

Initial production rate IP_rate Mmcf/d 3 4.5 6

Initial Decline rate ID_rate % per year 70 70 70

Estimated Ultimate Recovery EUR bcf per year 3 4.5 6

Table 2.dLow Average High

Capital expenditures CAP_EX $million 9 7.5 5Operational expenditures OP_EX $/mcf 2 1.5 1.5

Royalty rate Disc_rate % per year 23-35% 23-35% 23-35%

Gas price GP $/mcf 2 5 8

Discount rate Disc_rate % per year 10 10 10

Corporate tax rate Inc_Tax % per year 25 25 25

Development scenarios

Table 1.d Wells production performance

Montney (BC, Canada)

40

According to Enersight, drilling a well in the Montney play costs 5.8 million dollars, and so based on this fact, we have set a range of 𝐶𝐴𝑃_𝐸𝑋 scenarios between 5 and 9 million dollars for high and low development scenarios, respectively. As already affirmed, 𝐶𝐴𝑃_𝐸𝑋 are majorated for the only purpose of incorporating some other types of unaccounted costs. The same logic applies to 𝑂𝑃_𝐸𝑋�. According to Enersight, those costs are strictly superior to 1$/mcf simplifying our choice of setting a range of operating expenditures. In our project, we assume that 𝑂𝑃_𝐸𝑋� range between 1.5 and 2$/mcf.

For simplicity reasons, we assume that Montney’s natural gas is priced based on the Henry Hub, NYMEX forecasts and not the AECO (Alberta Gas Trading Price). Magyar and Jordan (2009) points that the fiscal model (in terms of royalty and tax regime) in Alberta range between 5 and 50% (an average royalty rate of 22.5%). Thus, all along our analysis, we compute the economics of the Montney play while supposing an average fixed royalty rate of 23% (≈22.5%) under all development scenarios. Finally, our choice of corporate income tax (federal tax) was at certain degree arbitrary given that the fiscal system differs between British Columbia and Alberta, so we had to choose among 2 different federal tax rates. We offset this problem by setting a somehow average tax rate of 25%18. Once again, the assumptions made in the case of the Montney shale are somehow hazardous, especially those assumptions in terms of federal taxes and royalty rates, and the results found should be understood.

18Magyar, D. and Jordan, C., 2009. “Exploring the economics of a Montney Shale Gas Development on Both sides of the border - BC versus Alberta”, Unconventional Gas Conference, CSUG, Well Spring.

41

2.2.5. Utica

The Utica shale basin is a sedimentary rock basin also known as “the Saint-Lawrence sedimentary basin” located in Quebec, Canada. Since 2006, this particular shale formation started to gain real momentum and to reveal some positive signs concerning its capacities to produce shale gas, economically. The latter is estimated to have a geographical extent of about 2344 square miles and a GIP capacity of 25-160 bcf per section (Lin 2010). Nowadays, the Utica shale is still in its early stages of development and its real production capacities are still largely unknown. Tables 1.e and 2.e below present the expected well performance for Utica wells and shale gas model parameters and their expected ranges, respectively.

We assume a 6 Mmcf/day and a 2 Mmcf/day IP rates for Utica P10 and P90 wells, respectively. The latter data computes the exact IP rates registered by St-Edward and Gentilly wells drilled in the Utica formation, respectively. EUR ranges and initial decline rates can be found in (Lin 2010). Lin (2010) point that capital expenditures (drilling and completion costs) in the Utica play are most likely to vary between 5 and 7 million dollars and operating expenditures will often range between 1 and 2$ per mcf.

Thus, we assume that 𝐶𝐴𝑃_𝐸𝑋 vary between 5 and 7.5 million dollars which is logical and we adopt the same range for 𝑂𝑃_𝐸𝑋� as it is assume in Lin (2010). In our project, we use in our calculation Quebec’s new royalty regime. Previously royalty rates in Quebec varied between 10 and 12.5%. Presently, according to the Ministères des Finances, royalty rates range between 5 and 35% depending on the volume of gas produced and on prevailing natural gas prices19. Finally, the corporate tax rate is set

19Quebec’s new royalty regime will enter into force once the strategic environmental assessment (ÉES) that was recommended by the BAPE has been completed and the legal and regulatory framework adapted to its conclusions.

Variable Code Unit P90 P50 P10

Initial production rate IP_rate Mmcf/d 2 4 6

Initial Decline rate ID_rate % per year 72 72 72

Estimated Ultimate Recovery EUR bcf per year 2.5 4 5.5

Table 2.eLow Average High

Capital expenditures CAP_EX $million 7.5 6 5

Operational expenditures OP_EX $/mcf 2 1.5 1

Royalty rate Roy_rate % per year 5-35% 5-35% 5-35%

Gas price GP $/mcf 2 5 8

Discount rate Disc_rate % per year 10 10 10

Corporate tax rate Inc_Tax % per year 25 25 25

Development scenarios

Table 1.e Wells production performance

Utica (Québec, Canada)

42

randomly. We suppose that 25% is the yearly monetary fraction of gas produced and commercialized that will go to the government.

Liu (2008) points that the combined federal and provincial income tax rate in Quebec amounts to 30.9%, however, tax credits can eventually range between 20 and 40%. The latter shows that the fiscal regime in Quebec is relatively more attractive when compared to other fiscal regimes in the U.S. and the rest of Canada. The following part of our second chapter demonstrates and discusses the economic results of our cash flow sensitivity analysis.

2.3. Economic discussion Refer to Appendix (A.3. Results - Sensitivity Analysis). Technically, shale gas wells with high IP rates have greater potentials to produce natural gas relatively to shale gas wells with lower initial production rates (Jeff Ventura, Aubrey k. McClendon and al. 2009). Thus, based on our prefixed assumptions, Haynesville wells will probably produce much more gas than it is the case for Marcellus wells, for example, and will eventually register better economic performances. Nevertheless, higher initial decline rates mean that the majority of cumulative gas production will come early in the life of the formation. Thus, the first 2 years of the life span of a gas well will be largely decisive of its economic feasibility. This is also mainly the case of Haynesville wells.

The latter show the highest initial decline rates (85%) among other U.S. and Canadian shale plays. However, this is not the case for Marcellus wells (and at a certain degree for Barnett wells), where, whilst the production declines at a 70% rate after the first year, the latter will be associated to nothing but to 7% of the total amount of gas that could be recovered.

Thus, longer reserve recoveries or lower recovery rates will largely impact the overall economics of the play. In the case of Marcellus wells, much more time will be required to depict and evaluate the production life cycle as well as the economic potential of the formation and this is mainly caused by the existence of low recovery rates (Jeff Ventura, Aubrey k. McClendon and al. 2009). At this point, we proceed by checking whether or not the results of our economic analysis will eventually come in conformity with the above explanation of technical input parameters or else will show that the latter characteristic of shale plays economics aren’t the only basis that delineate the economic profitability of gas wells. The first (𝐺𝑎𝑠 𝑃𝑟𝑖𝑐𝑒, 𝐼𝑃 𝑟𝑎𝑡𝑒) sensitivity analysis table studies the impact of various market and production scenarios on NPV economics of shale plays average development profiles.

Table 3 (silver line) shows that at an average development profile (in terms of costs), Marcellus and Utica wells will have the lowest breakeven price (3$/mcf) followed by Barnett, Montney, and Haynesville wells, respectively. The viability of these results will depend upon the assumptions made at the launch of the analysis. However, a study conducted by Deutsche Bank in 2010, assessing the economics of five different shale plays (Marcellus, Hayneville, Barnett, Fayetteville and Woodford), shows somehow a similar result where Marcellus wells are estimated to have the lowest breakeven price (3.17$ per mcf) when compared to other shale plays.

43

In a low economic environment where natural gas prices range between 2 and 4$/mcf, Montney and Haynesville wells fail to breakeven on a half-cycle basis. The rest of the plays succeed to breakeven but the registered NPV economics will relatively be low, and at a maximum wells performance scenario, only Barnett and Marcellus wells succeed to register IRR that exceed the pre-fixed 10% cost of capital of the producing company. More precisely, the IRR will be greater for the Marcellus than for other U.S. shales and this will most probably derive from the existence of premium natural gas pricing due to location and relatively low royalties in Appalachia. The same conclusion can be found in the Deutsche Bank report in 2010 and in Ventura (2009). The second (𝐺𝑎𝑠 𝑃𝑟𝑖𝑐𝑒, 𝐶𝐴𝑃_𝐸𝑋) sensitivity analysis assesses three wells production performance profiles (P10, P50, and P90) NPV economics of shale plays under various economic environments. Results found are summarized in Table 3 (blue line).

In a low economic environment, 2 $/mcf gas price, no value is created under most 𝐶𝐴𝑃_𝐸𝑋 scenarios for all shale plays except for Marcellus P10 wells that could be brought in for less than 3 million dollars. For P50 wells, profitability windows shrink and all shale plays fail to breakeven at a 2$/mcf gas price. At a gas price of 4$/mcf, zero value is created for most shale plays except for Marcellus and Barnett P50 wells that can be brought in for less than 3.5 and 4.4 million dollars, respectively. However, at a gas price of 6$/mcf, and under average 𝐶𝐴𝑃_𝐸𝑋 scenarios, almost all shale plays P50 wells succeed to breakeven and to create value. Thus, a 6$/mcf represents a favorable economic environment and as long as producing companies can maintain its drilling and completion costs at an average rate all shale plays P50 wells will be marginally profitable.

Table 3: Shale plays breakeven prices ($ per mcf), under various input-parameters combinations:

*All the results (breakeven prices) computed in this table are rounded.Shale_plays:

Marcellus Barnett Haynesville Montney Utica

4* 6* 5* 3*

3. P90 production profiles

(Gas Price , IP rate ): -under average cost scenario-

1. P10 production profiles

2. P50 production profiles

5*

(Gas Price, CAP_EX ): -Impact of First Year Price (FYP)-

2* 4* 6*

1. FYP (3$/mcf) and the rest varies between 2 and 8$/mcf

2. FYP (8$/mcf) and the rest varies between 2 and 8$/mcf

Sensitivity_Analysis_Results:

5*

4*

6*

2* 3*

7* 8*

3*

4*

3*

4*

2*

(Gas Price, CAP_EX ):

The combinations of input_parameters tested:

3*

44

In a somehow moderate economic environment where gas prices range between 3 and 5 dollars per mcf, most shale plays P10 wells succeed to breakeven if wells come at average 𝐶𝐴𝑃_𝐸𝑋 scenarios. More precisely, at 5$/mcf, value is created for Marcellus, Barnett, and Utica P10 wells under all 𝐶𝐴𝑃_𝐸𝑋 scenarios and for almost all Haynesville and Montney P10 wells except those that can’t be brought in for less than 10 and 8 million dollars, respectively. Finally, for all 𝐶𝐴𝑃_𝐸𝑋 scenarios depicted, all shale plays P10 wells succeed to breakeven when gas prices range between 6 and 8$/mcf. At a 6$ per mcf gas price, almost all Barnett and Marcellus P50 wells succeed to breakeven on a full 𝐶𝐴𝑃_𝐸𝑋 cycle. However, the rest of shale plays P50 wells only succeed to breakeven under average 𝐶𝐴𝑃_𝐸𝑋 scenarios. Moreover, at 8$/mcf gas price and higher, all shale plays P50 wells succeed to breakeven and to create value. Table 3 (blue line) summarizes the breakeven prices of all shale plays P10, P50, and P90 wells, respectively.

Our analysis also shows that, under the most (𝐺𝑎𝑠 𝑝𝑟𝑖𝑐𝑒,𝐶𝐴𝑃_𝐸𝑋) optimistic scenario, all shale plays P10 wells succeed to breakeven in less than a one-year period. Furthermore, under the same optimistic scenario, our sensitivity analysis results show that Marcellus P10 wells project economics register the highest IRR (≈120%) followed by Barnett (≈93%), Hayneville (55%), Utica (54%), and Montney (50%) P10 wells. Finally, in a moderate economic environment where natural gas price is 5$/mcf, all shale plays P90 wells fail to breakeven and to make money except for Marcellus wells that can be brought in for less than 4.5 million dollars (exceeds the average development profile in terms of costs) which is most likely unachievable.

In a more optimistic economic environment, where natural gas prices range between 6 and 8$/mcf, all shale plays P90 wells succeed to breakeven (with relatively low NPV project economics) under low 𝐶𝐴𝑃_𝐸𝑋 scenarios except for Utica P90 wells that fail to breakeven under all 𝐶𝐴𝑃_𝐸𝑋 scenarios, none of shale plays P90 wells succeed to breakeven and to create value under high 𝐶𝐴𝑃_𝐸𝑋 scenarios except for Marcellus wells, and only few succeed to breakeven under average 𝐶𝐴𝑃_𝐸𝑋 scenarios (Haynesville, Marcellus and Barnett).

Given the fact that shale gas production rates will decline steeply once the well is brought on-line; one of the most important factors that will delineate the potential profitability of shale gas wells is the price of the commodity during the first year of production (Kaiser 2012). In our last sensitivity analysis table, we endeavor to assess the impact of first year prices (FYP) on all shale plays P50 wells NPV project economics. We propose two (𝐺𝑎𝑠 𝑃𝑟𝑖𝑐𝑒,𝐶𝐴𝑃_𝐸𝑋) scenarios where first year gas prices are 8$/mcf and 3$/mcf, respectively. The prices for the following years range between 2 and 8$/mcf. All other model assumptions are the same as in (𝐺𝑎𝑠 𝑝𝑟𝑖𝑐𝑒,𝐶𝐴𝑃_𝐸𝑋) sensitivity analysis for P50 wells.

Comparing results, we realize that profitability windows will increasingly expand for all shale plays when gas prices are inferior to 8$/mcf. For example, Haynesville P50 wells register a NPV of 6 million$ at a price of 8$/mcf when 𝐶𝐴𝑃_𝐸𝑋 are 6 million$. Taking the same (𝐺𝑎𝑠 𝑃𝑟𝑖𝑐𝑒,𝐶𝐴𝑃_𝐸𝑋) combination, at a first year gas price of 8$/mcf, Haynesville P50 wells almost register the same NPV level (5.9 million$). However, if we consider the (5$, 6million$) combination, we realize that the NPV increases from 1.4$ million to 3.1$ million which is approximately an increase of 121% in NPV due to the first year price differential.

45

Similarly, if we consider the case where the first year price is 3$/mcf (same 𝐶𝐴𝑃_𝐸𝑋 scenario), we realize that the NPV decreases from -0.5 to -1.7 million$ which is approximately a decrease of 240% in NPV and this decrease is also due to the first year price differential.

The same logic applies to all shale plays P50 NPV project economics whilst the impact can differ from one play to another and the increase or decrease in NPV will depend upon several factors where the most important one is the initial decline rate. Therefore, it’s most likely that the FYP will have the largest impact on Haynesville P50 wells project economics (with the highest initial decline rate of almost 85%). More precisely, the cumulative production curve of Haynesville wells shows that more than 25%

( ��.�

× 100) of GIP will be recovered during the first-year life span of the well and almost more than 73%

(�.��.�

× 100) of GIP will be recovered in a 10-year period. Refer to appendix (A.4. Type Curves under 3

different production scenarios). Hence, it’s logical to say that higher or lower FYP will have the largest impact on Haynesville wells NPV project economics (relatively to other shale plays). Table 3 (red line) summarizes the impact of both 8$ and 3$/mcf first year prices on shale plays P50 wells breakeven prices and shows the incremental value they provide.

On one hand, at a first year price of 8$ per mcf, all shale plays P50 wells succeed to breakeven under high and average 𝐶𝐴𝑃_𝐸𝑋 scenarios. Profitability windows for all shale plays expand and almost all shale plays P50 wells succeed to breakeven at a price of 2$ per mcf under low 𝐶𝐴𝑃_𝐸𝑋 scenarios at the exception of both Montney and Utica P50 wells which breakeven at a price of 3$ per mcf.

Moreover, the analysis shows that at a FYP of 8$ per mcf Marcellus and Haynesville P50 wells show the highest NPV increases followed by Utica, Barnett, and Montney P50 wells, respectively. On the other hand, at a 3$/mcf FYP, the economic results are at a certain degree disastrous. Most of shale plays P50 wells fail to breakeven in a moderate economic environment under average 𝐶𝐴𝑃_𝐸𝑋 scenarios except for Barnett and Marcellus P50 wells which breakeven at a price of 4$ and 2$/mcf, respectively. Under high 𝐶𝐴𝑃_𝐸𝑋 scenarios, most of shale plays P50 wells fail to breakeven even under favorable economic environments and only Barnett and Marcellus P50 wells succeed to breakeven at a price of 6$/mcf or higher. Finally, at a 3$/mcf FYP, the profit window shrinks enormously for most P50 wells and even those wells that succeed to breakeven will relatively generate insufficient economic returns to recover the well and stimulate drilling and investment activities.

46

2.4. A logistic distribution model of wells deployment (Utica Shale) Economic performance is determined by several factors and to ensure a rigorous and pragmatic assassement, it is necessary to incorporate all relevant variables that contribute to the delineation of the economic profitability of shale plays. Among several economic and technical parameters, the pace of drilling or the rhythm at wich drilling activities are set is a crucial factor that forms the basis of an economic shale play once the first gas well is brought on stream. The results of our cash flow sensitivity analysis have proved that some shale plays could be more profitable than other plays, however, this doesn’t mean that the latter are less economic than those with highest NPV. Usually, to ensure the profitability of their investments, producing firms tend to maintain high average gas production rates over time.

To do so, a target number of wells (to be drilled) is set on a yearly basis, so that high production rates of continuously new wells brought on stream offset decreasing production rates of previously drilled wells.

Figure 7: Comparison of number of wells drilled (per year). Source: IHS Energy, Mackie Research [Fayetteville (2004-2009), Utica (2010)]

For the Utica shale, the latter is still in its early stages of development and the pace of drilling activities in it are still very slow when compared to other shale plays. Refer to Figure 7. To date, more than 4,000 wells were brought on-line in the Fayetteville play, up to 13,000 wells were identified by the rail road commission in the Barnett play, and even more than 1,200 wells were drilled in the Haynesville well (a relatively new shale play and could be compared to the Utica shale), and only few wells were drilled in the Utica play (Bailey 2010). Generally, the Utica shale is seen to have robust economics, however, nowadays, producing companies are still at a certain degree suspicious about its capacities to produce shale gas economically. This fact is aggraved by the absence of both social acceptance and oilifield services in the province, and the existence of remaining large quantities of unproven reserves (Andrew Potter, Helen Chan and al. 2008; Lin 2010).

47

All things considered, we establish a model that simulates the impact of volumes of gas produced, revenues, and royalties on the scale of the industry, through wells deployment scenarios in the Utica shale. The following section describes the model and the assumptions related to it.

2.4.1. Model and Assumptions The model presumes that all wells performances will be mainly dependent on the variables, initial production rates, hyperbolic exponent, initial decline rates and natural gas prices, that we applied in our cash flow analysis. Moreover, the model assumes that zero wells are drilled in year 0 and all wells are drilled post-deployment time. The latter is variable, however, at first, we assume that it is equal to 10 years. Besides, our simulation model represents royalty rates as a positive function of both volumes of gas produced and prevailing natural gas prices over time. This is justified because different generations wells have different performances and productivities and eventually will be subject to different royalty rates.

Our model uses a logistic growth model to allocate the deployment of 𝑋 wells over 𝑇 periods. Historically, the logistic function (curve) or growth model was developed20 and used in 1838 to model the S-shaped behaviour of growth of some population P over time t. The curve is designed so that it goes through three different stages: (1) the initial stage of exponential growth, (2) the saturation stage of growth where the curve goes through an inflexion point at which it changes from being concave upwards to concave downwards or vice versa, and finally (3) the maturity stage where the growth stops and the curve takes the shape of an horizontal line21.

In our model, we search to calculate a logistic function that computes the number of wells to be deployed over 𝑛 periods (years). We assume that the percentage of completed wells versus time follows a logistic function 𝐹:

𝐹 =1

1 + exp(−𝑡) (1.17)

Generally, the logistic function has no predetermined boundaries between which the calculated probabilities can vary the reason why we truncate our model by defining a lower and an upper bound for it [−𝑧, +𝑧]. The function equals 0% when 𝑡 < −6 and almost 100% when 𝑡 > 6.

Afterwards, we associate the values 𝑛 = 1 and 𝑛 = 𝑇 − 1 to the lower and upper limits [−𝑧, +𝑧] of our logistic distribution model, respectively. The below function depicts the time-based rhythm following which the wells are going to be established. It takes into account the target number of wells -to be drilled- during a predetermined time horizon 𝑇.

𝑚(𝑛) = 𝑧2𝑛 − 𝑇𝑇 − 1

(1.18)

20Logistic model was initially developed by Belgian mathematician Pierre Verhulst in 1838. 21McGinley and Lloyd (2010). "Logistic Growth" In: Encyclopedia of Earth. Eds. Cutler J. Cleveland (Washington, D.C.: Environmental Information Coalition, National Council for Science and the Environment). [Retrieved November 28, 2012] “http://www.eoearth.org/article/Logistic_growth.”

48

𝑧 is determined while we assume that the first well will be established in year 1, and so our equation can be written as:

11 + exp(𝑧) = 1 (1.19)

From the above, 𝑧 = ln(𝑋 − 1) and 𝑋 = 1. The first well is established in year 1.

The continuous exponential trend of wells drilled could be computed using the below equation. We affect a negative sign to 𝑚(𝑛), so the term inside the parenthesis becomes positive, which is logical because the rate at which the wells are going to be drilled over time will be increasingly positive before it reaches a peak point when a decision to stop drilling has been taken. After replacing 𝑧 by its value (see equation(1.18)), equation (1.19) can be rewritten as:

exp�−𝑚(𝑛)� = exp �−𝑧2𝑛 − 𝑇𝑇 − 2

� = exp �𝑧𝑇 − 2𝑛𝑇 − 2

� = (exp(𝑧))𝑇 − 2𝑛𝑇 − 2

exp�−𝑚(𝑛)� =(𝑋 − 1)(𝑇 − 2𝑛)

𝑇 − 2 (1.20)

And so, our cumulative distribution function of wells drilled, 𝐹(𝑛,𝑇,𝑋) can now be computed as follows:

𝐹(𝑛,𝑇,𝑋) =𝑋

1 + (𝑋 − 1)�������

(1.21)

Where 𝑛, 𝑇 and 𝑋 denote the total number of periods of the project, the depolyment time of wells and the target number of wells (to be) drilled, respectively. However, equation (1.21) presents some limits and needs to be specified. If 𝑋 = 1 and as already noted the first well will be established in year one (𝑛 = 1):

𝐹(𝑛,𝑇,𝑋) =1

1 + (1 − 1)������

= 1 (1.22)

This result is viable if and only if 𝑇 = 2𝑛. If 𝑇 = 𝑛 and the first well will be established in year 1, our cumulative distribution function wil not hold and will be undefined:

𝐹(𝑛,𝑇,𝑋) =1

1 + (1 − 1)������

=1

0� (1.23)

An so, to avoid this issue we impose 𝐹(𝑛,𝑇, 1) = 1.

If 𝑛 ≥ 𝑇 our model will be meaningless because all the wells are already established and in this case we impose that 𝐹(𝑛,𝑇,𝑋) = 𝑋. If 𝑇 = 1, we obtain:

𝐹(𝑛, 1,𝑋) =𝑋

1 + (𝑋 − 1)���� (1.24)

49

And so, 𝐹(0,1,𝑋) = 𝑋 − 1 and 𝐹(1,1,𝑋) = 1. The latter means that all the wells but one are established in year 0 and the remainder is established in year 1.

Thus, to avoid having all wells build in year 0 we impose that 𝐹(0,1,𝑋) = 0 and 𝐹(1,1,𝑋) = 1. Therefore, zero wells will be built in year 0 and the first well will be established in year 1. If 𝑇 = 2, we have an issue of a mathematic nature similar to when we had to impose that 𝑇 = 2𝑛. In this case if 𝑛 = 1, the cumulative distribution function is:

𝐹(1,𝑇,𝑋) =𝑋

1 + (𝑋 − 1)������

(1.25)

Resulting in 𝐹(1,𝑇,𝑋) = 1 even if 𝑇 = 2 (using l’Hôpital’s rule). So, the first well is build in year 1 and the rest of the wells are build in year 2. Using Excel and Visual Basic (VB), we simply program our model by including the input variables in a way that only half of the wells (relatively to the total target number of wells) will be build in year 1. To note that our programming (using VB) was calibrated in a manner that one well is established in year 1. Thus, it would have been preferable if we could specify a minimum percentage of wells (𝑋 > 1) to be drilled in year 1. However the latter couldn’t be done and the total number of wells drilled in year 1 was randomly set.

Finally, our choice of a logistic distribution model was arbitrary but precise. The latter is extensively used in different areas. It is oftenly used as a tool for modelling continuous variables in linear regressions. Moreover, we figured out that the evolution of the number of wells that we have observed in other shale plays could also be depicted using a similar logistic-based function, what justifies our choice of the model.

50

2.4.2. Economic discussion We define a scenario of wells deployment that corresponds to the below input parameters (reference scenario) . By simulating some of the model key input variables: Gas Price (𝐺𝑃), Target number of wells (𝑋), deployment time (𝑇), etc. we attempt to describe how those input variations will potentially impact average royalty rates, annual royalties paid, and volumes of gas production, respectively.

Reference scenario: 𝑛 = 20 years, 𝑇 = 10 years, 𝑋 = 250 per year, IP rate = 8641 Mmcf per month, 𝑏 = 1.2, ID rate = 5.41% per month, and natural gas price = 8$/mcf. Figures (a), (b) and (c) represent volumes of gas produced over a 20-year period, annual amounts of royalties paid to the government and wells count and cumulative distribution of wells, repectively. Figure (d) is computed from figures (a), (b) and (c). It summarizes the evolution of average royalty rates over time.

(a) (b)

(c) (d) Figures (a) and (b) show that volumes of gas produced and annual royalties will both be bell-shaped under 𝐹(20, 10, 1500) deployment scenario (or any deployment scenario). This conclusion is somehow logical. Annual royalties paid increase with volumes of gas produced, so everytime more wells are brougth on-line, volumes of gas produced will grow at a constant rate and so will do annual royalty rates. The more gas is produced, the more royalties will be paid and vice versa. However, it’s important to mention that the increase will not be necessary proportional. Volumes of gas produced will increase based on the rate at which drilling is taking place (number of wells drilled per year and the marginal

51

productivity of every additional well drilled) and the increase in annual royalties will largely depend of the marginal productivity of every (𝑋 + 1) well brought on stream.

In our analysis, we suppose that prices are constant over time and the decrease in average royalty rates (and eventually, in annual royalties paid) will probably be due to the decrease in volumes of gas produced over time. Therefore, because gas production from shale plays deplete rapidly during the early life of the well, to maintain an average constant rate of gas production, more wells need to be drilled on annual basis, so that every new well brought on stream will offset the production decline of previous wells.

The variable 𝑛 (total number of years) represents the bottom diameter (𝑥 𝑎𝑥𝑖𝑠) of the bell-shape for both figures (a) and (b) so, every time 𝑛 increases the diameter of “the bell” will also increase in the same proportion. We also remark, from both figures (a) and (b), that volumes of gas produced and annual royalties paid increase for a certain period of time, peak, than decrease before going steady for a long period of time at low production growth rates. The area that separates the start of the production and the peak point is mainly representative of our choice of 𝑇 (deployment time). Thus, both volumes of gas produced and annual royalties paid peak after 10 years of gas production when drilling activities are terminated. However the curvarture of the figures is determined by our choice of both variables 𝑇 and

𝑋. In our case, 1500 wells are deployed over a 10-year period. The fraction ���� is determinant of the top

shape of both figures (a) and (b). If ��→ 0, the peak point of the bell will tend to be sharp, however,

if ��→ 1, the latter will rather be rounded and the distribution will correspond to a more normal bell-

shaped function. Refer to Fig. (e) and (g). Therefore, the shape and curvarture of both figures will be affected by how 𝑋 and 𝑇 will be eventually allocated in proportion to 𝑛. 𝑋 will probably be decisive of the rate at which volumes of gas produced and annual royalties paid increase. Under all cited conditions, the diameter of the bell will always remain unchanged denoting the total number of years (𝑛).

(e) (f)

In figure (c), the S-shaped curve represents the cumulative distribution function of wells (over time) that derives from our logistic model. Wells deployment are set to be drilled over a 10-year period.

52

The drilling starts in 2016 and ends in 2026, and the S-shaped curvature is dependent on how the deployment of wells will be modelled. In our case, we suppose that the majority of wells will come in years ranging between 𝑡 + 2 ≤ 𝑇 ≤ 𝑡 − 2 and so, if 𝑡 = 1 denotes the year 2017 (2016 doesn’t count because zero production is available at 𝑡 = 0), the majority of wells will be brought on stream during 2019 ≤ 𝑇 ≤ 2024 what justifies the S-shape of our cumulative distribution function of wells as well as the existence of horizontal lines at the start (+ 2 𝑦𝑒𝑎𝑟𝑠) and at the end (− 2 𝑦𝑒𝑎𝑟𝑠) of figure (c). As

pointed above, for the case of both figures (a) and (b), when ��→ 1, the cumulative distribution function

will also be affected and will tend to have a more perfectly rounded (normal) S-shaped curve. Refer to figures (f) and (h). Finally, figure (d) simply denotes that as long as drilling activities are continuously increasing, volumes of gas produced will also increase, and average royalty rates paid will tend to be high, however, when drilling activities stop and gas production starts to sharply decline, average royalty rates will also tend to decrease to finally reach a 10% minimum value in year 20.

(g) (h)

(i) (j) Finally, we prove that when 𝑇 → 𝑛, annual royalties will form an average steady and continuous (over 𝑛) source of financing to the government relatively to the case when 𝑇 ≪ 𝑛. To prove it, we assume an average natural gas price of 6 $/mcf, 𝑋 = 1500 wells,𝑇 = 6 years, and 𝑛 = 20 years.

53

We notice that annual royalties peak at 800 million$, however if we follow the linear trend showing in figure (i) we realize that average annual royalties have a maximum value of 250 million$ at the start of production (year 2016) and decline over time to reach a 0% value after 20 years of production (year 2036). In this case, (�

�= 0.3), government could benefit from high royalty returns for a short period of

time before the latter decrease dramatically.

However, if the governement is willing to benefit and to maintain relatively high average rate of returns from annual royalties throughout the life cycle of the project, drilling activities have to be allocated differently so that �

�→ 1. See linear trend in figure (j), where 𝑇 = 15 years. The latter shows that annual

royalties maintain an approximate constant average level of 100 million$ (in annual royalties) over the life cycle of the project. In this case, the government will continuously receive almost the same relatively high average of annual royalties during almost all the life cycle of the project. Moreover, if more (less) wells were to be brought on stream, the linear trend will tend to shift parallelly to the top (bottom) and at a certain degree to the right (left) in both cases.

Lastly, our analysis proves that total governemental revenues from annual royalties as well as their cumulative distribution over time will depend in particular on both (among others) the number of wells (𝑋) that will be brought into production and the way the deployment time (𝑇) of wells will be allocated relatively to the total number of periods of the project (𝑛). The main idea behind this is that royalty rates increase with volumes of gas produced, however, in shale gas, we showed that volumes of gas production tends to deplete rapidly during the early life of every well drilled therefore the only means remaining for operators to maitain steady growth rates of gas production and thus realtively high average royalty rates over 𝑛 is by continuously drilling more and more wells (increasing 𝑋) and ensuring that 𝑋 will be normally distributed so that 𝑇 → 𝑛. As a result, if drilling activities (𝑋) within the industry were most likely to increase gradually over time, so will do annual royalties collected, and royalty shares in governemental revenues will certainly remain modest until a real boost in drilling activities is brought on track.

54

Conclusion

Our study is limited in some important ways and the results found should be understood: (1) the viability of the results found depends upon the pre-fixed model assumptions, (2) we ignore certain costs such as: depreciation (amortization), allowance costs, etc. (3) we do not differenciate between the production of wet and dry gas while dry gas is cheaper to produce than wet gas (kaiser 2012), (4) volumes of gas produced are unknown and are, hence estimated using Arps decline curves, therefore an (over)under estimation of gas production can occur, and (5) drilling & completion costs, royalties, etc. are all collected from the public domain and are known only approximately.

Although, despite the potential gaps that our study may include, our analysis shows that the economic sustainability of shale gas production is largely sensitive to four main factors: (1) the geologic properties of the play or reservoir, (2) technological advances, in terms of drilling and completion of wells, (3) the path at which drilling activities are taking place, and (4) the economic environment under which firms choose to operate. We examined the economics of producing shale gas from five different shale plays using production decline curve analysis and breakeven economics and we defined the various conditions under which wells in every shale play are expected to be profitable on an after-tax basis. We also showed that the economic profitability of wells depend on the existence of both good economic and geological (conditions) properties. This was proven by the fact that despite that Haynesville wells presented better geologic properties than Marcellus wells; the latter had better economics than the former due to the existence of high drilling and completion costs in Louisiana relatively to Appalachia.

Moreover, we examined the profit envelopes of Marcellus, Barnett, Haynesville, Montney, and Utica wells, respectively under different development scenarios and the results came as follow: Marcellus and Barnett P10 wells had the best economic performances under most development scenarios among the five wells tested in our model. In a low economic environment, our analysis showed that most P90 and P50 wells fail to breakeven on a full cycle basis except for Marcellus wells which can be brought on stream for less than 4.5$ million. However, in a high economic enviroment most P10 wells (for all shale plays) registered good economics and succeeded to breakeven under average 𝐶𝐴𝑃_𝐸𝑋 scenarios. As for our -First Year Gas Price- sensitivity analysis, we showed that at a first year high economic environment all P50 wells succeeded to breakeven even under high 𝐶𝐴𝑃_𝐸𝑋 scenarios. Consequently, we concluded that first years of production are critical and significantly influence the marginal economics of shale plays which are initially driven by the tradeoff between high initial production, steep decline, high investment, and prevailing economic conditions (Kaiser 2012).

Finally, as for our logictic growth model, we showed that because the Utica shale is still in its relative infancy, its economic success remains largely uncertain. However, even though it is still experimental and presents higher risks relatively to other shale plays, we noticed that it is best situated in terms of gas price realizations and royalties, and has a relatively easy topography; all togother, this suggest that the latter could generate high average rate of returns if production growth and the path of drilling activities increased and good well results were achieved (Andrew Potter, Helen Chan and al. 2008).

55

Appendix A.1. Decline Curves p. 56

A.2. Quebec’s New Royalty Regime (NRR) p. 61

A.3. Results - Sensitivity Analysis p. 62

A.4. Type Curves under 3 different production scenarios p. 86

56

A.1. Decline Curves

Historically, reservoir engineers used to rely on simple extrapolation techniques to estimate oil and gas reserves from conventional formations. By extrapolation techniques, we mean the estimation of the value of a certain variable outside a known range (forecasting future production) from values within that range by assuming that the estimated future value will logically follow from the known past ones (based on past production performances). Thus, it's simply question to predict future production performances by extending past production trends to nelwy discovered wells.

Arps (1944) was the first to define a set of mathematical equations (three type curves) that could depict how conventional reservoirs will futurely behave in terms of future production rates based on past performance data. Mathematically, using the Arps technique to estimate shale gas reserves (from unconventional reservoirs) is impossible (𝑏 > 1) because it often leads to unreliable results. Refer to Table 4.

Figure 8: Truncation of hyperbolic equations Figure 9: Arps Type curves

In our project, we offset this issue by using the "exponential truncation of hyperbolic equations" method (Maley 1985). It mainly consists of converting (generally, at some arbitrary point) the production decline curve from an hyperbolic trend to an exponential one, therefore constraining the EUR to be finite. For alternative methods, see Spivey and al. (2001), Rushing and al. (2007), Cheng and al. (2008), and Valko & Lee (2010).

We assume that the decline curve switches from an hyperbolic decline to an exponential decline when the annual rate of decline in production falls to 7%. The same logic can be found in (Lake and al., 2012). This assumption applies to all shale plays analyzed in our project. In general, Arps decline curves (type curves) take three different shapes or curvatures based on the value of the hyperbolic exponent 𝑏:

𝑞� = 𝑞� × (1 + 𝑏𝐷�𝑡)���

Where:

𝑞� = the production rate in year t; 𝑞� = is a constant and denotes the initial production rate in year 0; 𝐷� = is a constant and denotes the initial decline rate in year 0;

57

b = is a constant and denotes the hyperbolic exponent (the rate at which the decline rate changes over time); 𝑞��(𝑞) = the time required to attain gas flow rate 𝑞; 𝑄� = EUR; 𝐷� = decline rate in year t; t = the time period for which the production is being estimated; 𝑛 = the backpressure slope and describes the drive mechanism inside the reservoir. Table 4 summarizes all the equations that we used in our project to estimate volumes of gas produced from every shale play subject of our study. The equations showing below was taken and adapted from (Cook 2005; Wahyuningish 2008; and Fekete associates Inc., Analysis methods, 2010). In the existing literature on decline curves, the current decline rate 𝐷, denotes the fractional change in rate per unit of time frequently expressed in “percentage per year” once gas production has started, is defined as follow:

𝐷 = − (∆𝑞/𝑞)/𝑡 = −(∆𝑞/∆𝑡)/𝑞

From the latter, the exponential decline, where 𝐷 varies at a constant rate and 𝐷� is the initial decline rate, is given by:

𝑞�𝑞�

=1𝑒���

→ 𝑞� = 𝑞� × 𝑒����

If 𝐷 is considered to be either hyperbolic or harmonic an exponent 𝑏 is incorporated into the equation of the decline curve to account for the changing decline rate. The new equation is given by:

𝑞�𝑞�

=1

(1 + 𝑏𝐷�𝑡)��

→ 𝑞� = 𝑞� × (1 + 𝑏𝐷�𝑡)���

For the hyperbolic decline and because 𝐷 doesn’t vary at a constant rate, the latter is given by:

𝐷 = 𝐾 × 𝑞�

𝑞 = Gas flow rate,

𝐾 = 𝐷𝑖 / (𝑞��) (a constant),

𝑏 = Constant that varies between 0 and 1.

And the relationship between 𝐷 and 𝐷� can be written as for 0 < 𝑏 < 1:

𝐷 =𝐷𝑖

1 + 𝑏𝐷𝑖𝑡

𝑏 = 0 → 𝐷 = 𝐷𝑖

𝑏 = 1 → 𝐷 =𝐷𝑖

1 + 𝐷𝑖𝑡

58

Table 4: Estimating gas reserves using Arps equations

Parameters Conventional Reservoirs Unconventional Reservoirs

b 0 0 < b < 1 b = 1 b > 1

Type curves Exponential Hyperbolic Harmonic Hyperbolic then Exponential

𝑞� 𝑞� × 𝑒���� 𝑞� × (1 + 𝑏𝐷�t)� ��

𝑞� × (1 + 𝐷�t)� �

Exponential truncation of hyperbolic equations

𝑞��(𝑞) ln (𝑞�𝑞 )

𝐷�

(𝑞� 𝑞� )� − 1

𝑏𝐷�

𝑄� 𝑞�𝐷�

× (1 − 𝑒����) 𝑆𝑎𝑚𝑒 𝑎𝑠 𝑓𝑜𝑟 (𝑏 > 1)

𝑞�𝐷�

× ln(1 + 𝐷�𝑡) 𝑞�

𝐷�(1 − 𝑏)�1 −

1

(1 + 𝑏𝐷�𝑡)�����

lim�→�

𝑄� 𝑞�𝐷�

× (1 − 𝑏)�� ∞

𝐷� = − 𝜕𝑞𝜕𝑡𝑞�

(𝑡) 𝐷�1 + 𝑏𝐷�𝑡

𝑛 = 𝑏

2(1 − 𝑏) 0 < 𝑛 < ∞ 𝑁𝐴

*These equations are conform to the existing literature in petroleum engineering on decline curves.

59

Having obtained the constants 𝑏,𝐷� and 𝑞� from a curve fit of the production data, the flow rate, at any time 𝑡, is given by:

𝑞� = 𝑞� × (1 + 𝑏𝐷�𝑡)���

The cumulative production 𝑄𝑡 for the hyperbolic decline can be written as:

𝑄� = �𝑞(𝑠)𝑑𝑠 =𝑞�

(1 − 𝑏)𝐷�× �(1 − 𝑞�

����� �

�

Now if we replace 𝑞� by its expression and make some adjustments to the above equation, we obtain:

𝑄� =𝑞�

𝐷�(1 − 𝑏) × �1 −1

(1 + 𝑏𝐷�𝑡)�����

This is true for 0 < 𝑏 < 1 and 𝑏 > 1. For 𝑏 = 1 (hamonic decline), 𝑄𝑡 can be rewritten as:

𝑄� =𝑞�𝐷�

× ln(1 + 𝐷�𝑡)

For the exponential decline (𝑏 = 0):

𝑄� =𝑞�𝐷�

× (1 − 𝑒����)

Now, if lim�→� 𝑄�, for different b values, 𝑄∞ can be written as:

For 𝑏 ≥ 1 then:

𝑄� = ∞

For 0 ≤ 𝑏 < 1 then:

𝑄� =𝑞�

𝐷�(1 − 𝑏)

To add that in order to calculate 𝑞�1(𝑞) denoting the time required to attain gas flow rate 𝑞 we consider 𝑞� = 𝑞, and so:

For 𝑏 = 0, we replace 𝑞 by its expression and we obtain:

𝑞 = 𝑞� × 𝑒��� → 𝑞��(𝑞) =𝑒��𝑞𝑖

(𝑞)

𝑞�1(𝑞) = ln (𝑞�𝑞 )

𝐷�

60

The same logic applies for 𝑏 > 0. It suffices to replace 𝑞 by its expression and make some adjustments to the equation to obtain:

𝑞�1(𝑞) = (𝑞� 𝑞� )� − 1

𝑏𝐷�

Finally, the mathematical expression of the backpressure slope 𝑛 developed by Fetkovich is given by:

𝑏 =2𝑛

2𝑛 + 1→ 𝑛 =

𝑏2(1 − 𝑏)

For 𝑏 > 1, 𝑛 cannot be calculated and a problem of division will occur, however for 0 < 𝑏 < 1 → 0 <𝑛 < ∞.

61

A.2. Quebec’s New Royalty Regime (NRR)

Table 5: Royalty rates table

Some additional notes:

(1) The rate is 35% for small businesses during the exploration phase. A rate of 18.75% or 38.75% is applicable for exploration in the Mid-North and Far North. The NRR is a non-refundable royalty credit for exploration such as exists in other jurisdictions. It only applies if gas is being produced: it thus serves as a production incentive.

(2) It helps to establish the industry and guarantees that the royalty system is competitive (i.e. granting incentives, etc.) with those of other jurisdictions. The 5% royalty rate applies when prices for the resource and production are low. The 35% royalty rate applies when prices and well productivity are high.

(3) The NRR makes it possible to more efficiently collect a portion of the value of the resource, while taking into account the economic parameters within which the industry operates.

(4) In the future, total revenues from royalties will depend on the number of wells brought into production (250 wells each year). The government anticipates that development in the industry will be gradual, and so there will be a gradual rise in revenues from royalties.

250 500 750 1000 1250 1500 1750 2000 2250 2500 2750 3000 3500 4000 4500 5000 5500 60003 5 5 5 5 8 11.4 14.8 18.2 21.6 25 25 25 25 25 25 25 25 254 5 5 6.1 9.5 13 16.4 19.8 23.2 26.6 30 30 30 30 30 30 30 30 305 5 7.7 11.1 14.5 18 21.4 24.8 28.2 31.6 35 35 35 35 35 35 35 35 356 9.2 12.7 16.1 19.5 23 26.4 29.8 33.2 35 35 35 35 35 35 35 35 35 357 11.7 15.2 18.6 22 25.5 28.9 32.3 35 35 35 35 35 35 35 35 35 35 358 14.2 17.7 21.1 24.5 28 31.4 34.8 35 35 35 35 35 35 35 35 35 35 35

Gas_Price 9 16.7 20.2 23.6 27 30.5 33.9 35 35 35 35 35 35 35 35 35 35 35 3510 19.2 22.7 26.1 29.5 33 35 35 35 35 35 35 35 35 35 35 35 35 3511 21.2 24.7 28.1 31.5 35 35 35 35 35 35 35 35 35 35 35 35 35 3512 23.2 26.7 30.1 33.5 35 35 35 35 35 35 35 35 35 35 35 35 35 3513 25.2 28.7 32.1 35 35 35 35 35 35 35 35 35 35 35 35 35 35 3514 27.2 30.7 34.1 35 35 35 35 35 35 35 35 35 35 35 35 35 35 3515 29.2 32.7 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35

*A FAIR AND COMPETITIVE ROYALTY SYSTEM - FOR RESPONSIBLE SHALE GAS PRODUCTION, Appendix, Table.12, p. 48, 2011-12.

Gas_Volume

62

A.3. Results - Sensitivity Analysis

63

(Gas Price , CAP_EX ) NPV (10%, $million) Sensitivity Analysis (P10 profiles):

2.5 3 3.5 4 4.52 -0.2 -0.7 -1.2 -1.7 -2.23 1.0 0.5 -0.1 -0.5 -1.54 2.1 1.6 1.1 0.6 0.15 3.2 2.7 2.2 1.7 1.26 4.4 3.9 3.4 2.9 2.47 5.5 5.0 4.5 4.0 3.68 6.7 6.2 5.7 5.2 4.7

CAP_EX ($million)

Gas Price ($/m

cf)

Barnett play

2.5 3 3.5 4 4.5 5 5.52 0.2 -0.2 -0.7 -1.3 -1.7 -2.3 -2.73 1.6 1.0 0.6 0.1 -0.4 -0.9 -1.44 3.0 2.4 1.9 1.4 0.9 0.4 -0.035 4.3 3.8 3.3 2.8 2.3 1.8 1.36 5.7 5.2 4.7 4.2 3.7 3.2 2.67 7.0 6.5 6.0 5.5 5.0 4.6 4.08 8.4 7.9 7.4 6.9 6.4 5.9 5.4

Marcellus play

6 6.5 7 7.5 8 8.5 9 9.5 102 -2.2 -2.7 -3.2 -3.7 -4.2 -4.7 -5.2 -5.7 -6.23 -0.3 -0.8 -1.3 -1.8 -2.3 -2.8 -3.3 -3.8 -4.34 1.60 1 0.6 0.09 -0.4 -0.9 -1.4 -1.9 -2.45 3.5 3 2.5 2 1.5 0.99 0.5 0.0 -0.56 5.4 4.9 4.4 3.9 3.4 2.9 2.4 1.9 1.47 7.3 6.8 6.3 5.8 5.3 4.8 4.3 3.8 3.38 9.2 8.7 8.2 7.7 7.2 6.7 6.2 5.7 5.2

Haynesville play

64

5 5.5 6 6.5 7 7.5 8 8.5 92 -1.8 -2.3 -2.8 -3.4 -3.9 -4.3 -4.8 -5.3 -5.83 -0.3 -0.8 -1.3 -1.8 -2.3 -2.8 -3.3 -3.8 -4.34 1.2 0.7 0.2 -0.2 -0.7 -1.2 -1.7 -2.2 -2.75 2.8 2.3 1.8 1.3 0.8 0.3 -0.2 -0.6 -1.26 4.3 3.9 3.4 2.9 2.3 1.8 1.3 0.8 0.37 5.9 5.4 4.9 4.4 3.9 3.4 2.9 2.4 0.98 7.5 7 6.5 6 5.5 5 4.5 4 3.5

Montney play

5 5.5 6 6.5 7 7.52 -1.7 -2.2 -2.7 -3.2 -3.7 -4.23 -0.1 -0.6 -1.1 -1.6 -2.1 -2.64 1.5 0.9 0.5 -0.09 -0.5 -15 3.1 2.6 2.1 1.6 1.1 0.66 4.7 4.2 3.7 3.2 2.7 2.27 6.4 5.9 5.4 4.8 4.4 3.98 7.9 7.5 6.9 6.4 5.9 5.5

Utica play

(Gas Price , CAP_EX ) IRR (NPV = 0, %) Sensitivity Analysis (P10 profiles):

2.5 3 3.5 4 4.52 -2.5 -7 -10.2 -12.5 -14.43 12.1 4.7 -0.4 -4.2 -74 27.6 17.2 10 4.7 0.75 43.5 30.3 20.9 14 8.86 59.7 43.6 32.2 23.7 17.37 76 57 43.6 33.6 25.98 92.4 70.6 55.1 43.6 34.7

Gas Price ($/m

cf)CAP_EX ($million)

Barnett play

65

2.5 3 3.5 4 4.5 5 5.52 2.9 -2.8 -6.8 -9.7 -11.9 -13.6 -15.13 21.2 11.9 5.4 0.7 -2.8 -5.6 -7.84 40.4 27.6 18.5 12 6.9 2.9 -0.25 60.0 43.7 32.1 23.6 17.1 11.9 7.86 79.7 60 46 35.6 27.6 21.3 16.17 99.6 76.5 60 47.7 38.3 30.8 24.78 119 93 74.1 60 49.1 40.4 33.4

Marcellus play

6 6.5 7 7.5 8 8.5 9 9.5 102 -11.3 -12.7 -13.9 -14.9 -15.8 -16.6 -17.3 -18.0 -18.63 -1.7 -3.9 -5.8 -7.5 -8.9 -10.1 -11.2 -12.2 -13.04 8.7 5.5 2.7 0.4 -1.6 -3.4 -4.9 -6.2 -7.45 19.6 15.4 11.8 8.7 6.1 3.8 1.8 0.0 -1.66 30.9 25.7 21.2 17.4 14.2 11.3 8.8 6.5 4.57 42.4 36.2 30.9 26.4 22.5 19.0 16.0 13.3 10.98 54.0 46.8 40.7 35.5 31.0 26.9 23.4 20.3 17.5

Haynesville play

5 5.5 6 6.5 7 7.5 8 8.5 92 -11.1 -12.7 -14.0 -15.2 -16.2 -17.0 -17.8 -18.5 -19.03 -2.0 -4.5 -6.6 -8.3 -9.8 -11.0 -12.2 -13.2 -14.04 7.8 4.2 1.3 -1.2 -3.3 -5.0 -6.6 -7.9 -9.05 18.0 13.4 9.5 6.3 3.6 1.3 -0.7 -2.5 -4.06 28.6 22.8 18.1 14.0 10.7 7.8 5.4 3.2 1.37 39.3 32.5 26.8 22.1 18.1 14.6 11.6 9.0 6.78 50.2 42.3 35.7 30.3 25.6 21.6 18.1 15.0 12.4

Montney play

66

5 5.5 6 6.5 7 7.52 -10.7 -12.4 -13.8 -15 -16 -173 -0.9 -3.6 -5.8 -7.7 -9.3 -10.64 9.7 5.8 2.6 0.0 -2.3 -4.25 20.7 15.6 11.5 8 5.1 2.66 32 25.8 20.7 16.4 12.8 9.77 43.6 36.2 30.2 25 20.7 178 55.2 46.7 39.7 33.8 28.8 24.5

Utica play

(Gas Price , CAP_EX ) Payout period (years) Sensitivity Analysis (P10 profiles):

2.5 3 3.5 4 4.52 NA NA NA NA NA3 3.0 5.5 NA NA NA4 1.8 2.5 4 5.5 105 1.2 1.8 2 3 46 1.0 1.2 1.5 2 2.27 <1 1 1.2 1.5 28 <1 <1 1 1.2 1.5

CAP_EX ($million)

Gas Price ($/m

cf)

Barnett play

2.5 3 3.5 4 4.5 5 5.52 7.0 NA NA NA NA NA NA3 2.0 3 5.5 10 NA NA NA4 1.5 2 2.5 3 4.5 7 NA5 1.0 1.2 1.5 2 2.5 3 4.26 <1 1 1.2 1.6 2 2.2 2.57 <1 <1 1 1.2 1.5 1.8 28 <1 <1 <1 1 1.2 1.5 1.8

Marcellus play

67

6 6.5 7 7.5 8 8.5 9 9.5 102 NA NA NA NA NA NA NA NA NA3 NA NA NA NA NA NA NA NA NA4 4.0 5 7 8 NA NA NA NA NA5 2.5 3 4 4.5 5 6 8 >10 NA6 1.5 2 2.5 2.8 3 3.5 4 5 67 1.0 1.5 2 2.5 2.8 3 3.2 3.5 3.88 <1 <1 1 1.5 2 2.2 2.5 2.8 3

Haynesville play

5 5.5 6 6.5 7 7.5 8 8.5 92 NA NA NA NA NA NA NA NA NA3 NA NA NA NA NA NA NA NA NA4 4.5 6 10 NA NA NA NA NA NA5 2.5 3 3.8 4.5 7 9 NA NA NA6 2 2.2 2.5 3 3.5 4.5 5 7 107 1.5 1.8 2 2.2 2.5 3 3.5 4 4.58 1.0 1.2 1.5 1.8 2 2.2 2.5 3 3.2

Montney play

5 5.5 6 6.5 7 7.52 NA NA NA NA NA NA3 NA NA NA NA NA NA4 4.0 5 7.5 >10 NA NA5 2.0 2.5 3.5 4 5.5 76 1.8 2 2.2 2.5 3 3.87 1.2 1.5 1.8 2 2.2 2.58 <1 1 1.5 1.8 2 <2

Utica play

(Gas Price , CAP_EX ) NPV (10%, $million) Sensitivity Analysis (P50 profiles):

68

2.5 3 3.5 4 4.52 -0.9 -1.4 -1.9 -2.4 -2.93 -0.1 -0.5 -1 -1.5 -24 0.7 0.2 -0.2 -0.7 -1.25 1.5 1 0.5 0.03 -0.56 2.3 1.8 1.3 0.8 0.47 3.1 2.6 2.1 1.6 1.18 3.9 3.4 2.9 2.4 2

CAP_EX ($million)

Gas Price ($/m

cf)

Barnett play

2.5 3 3.5 4 4.5 5 5.52 -0.3 -0.8 -1.3 -1.8 -2.3 -2.8 -3.33 0.7 0.2 -0.2 -0.7 -1.2 -1.7 -2.24 1.8 1.4 0.9 0.4 -0.1 -0.6 -1.15 2.9 2.4 1.9 1.4 0.9 0.4 -0.036 4.0 3.5 3.0 2.5 2.0 1.5 1.07 5.1 4.6 4.1 3.6 3.1 2.6 2.18 6.2 5.7 5.2 4.7 4.2 3.7 3.2

Marcellus play

6 6.5 7 7.5 8 8.5 9 9.5 102 -3.0 -3.5 -4.0 -4.5 -5.0 -5.5 -6.0 -6.5 -7.03 -1.5 -2.0 -2.5 -3.0 -3.5 -4.1 -4.5 -5.0 -5.54 -0.03 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 -3.5 -4.05 1.4 0.9 0.4 -0.03 -0.5 -1.0 -1.5 -2.0 -2.66 2.9 2.4 1.9 1.4 0.9 0.5 -0.04 -0.5 -1.07 4.4 4.0 3.4 2.9 2.4 1.9 1.4 1.0 0.48 5.9 5.4 4.9 4.4 3.9 3.4 2.9 2.4 1.9

Haynesville play

69

5 5.5 6 6.5 7 7.5 8 8.5 92 -2.6 -3.1 -3.6 -4.1 -4.6 -5.1 -5.6 -6.1 -6.53 -1.5 -1.9 -2.5 -3 -3.5 -4 -4.5 -4.9 -5.34 -0.3 -0.8 -1.3 -1.8 -2.3 -2.8 -3.3 -3.8 -4.35 0.8 0.3 -0.1 -0.6 -1.1 -1.6 -2.1 -2.6 -3.16 2 1.5 1 0.5 0.03 -0.4 -0.9 -1.4 -1.97 3.2 2.7 2.2 1.7 1.2 0.7 0.2 -0.3 -0.88 4.3 3.8 3.3 2.9 2.4 1.9 1.4 0.9 0.3

Montney play

5 5.5 6 6.5 7 7.52 -2.8 -3.3 -3.8 -4.3 -4.8 -5.33 -1.7 -2.2 -2.7 -3.2 -3.7 -4.24 -0.6 -1.2 -1.7 -2.1 -2.6 -3.15 0.4 -0.08 -0.5 -1 -1.6 -26 1.5 1 0.5 0.0 -0.5 -17 2.6 2 1.6 1 0.5 0.088 3.6 3.1 2.7 2.2 1.6 1.16

Utica play

(Gas Price , CAP_EX ) IRR (NPV = 0, %) Sensitivity Analysis (P50 profiles):

2.5 3 3.5 4 4.52 -10.6 -13.6 -15.8 -17.5 -18.83 -1.0 -6.0 -9.2 -11.7 -13.64 9.1 2.3 -2.4 -5.9 -8.55 19.8 11.0 4.7 0.2 -3.26 30.8 19.8 12.2 6.6 2.37 42.0 29.0 20.0 13.1 8.08 53.3 38.3 27.7 19.9 14.0

Barnett playCAP_EX ($million)

Gas Price ($/m

cf)

70

2.5 3 3.5 4 4.5 5 5.52 -4.0 -8.3 -11.4 -13.6 -15.4 -16.8 -17.93 10.1 2.9 -2.0 -5.6 -8.3 -10.4 -12.24 25.0 15.0 8.0 2.9 -0.9 -4.0 -6.35 40.4 27.6 18.6 11.9 6.9 2.9 -0.26 56.1 40.4 29.4 21.3 15.0 10.1 6.27 71.8 53.4 40.4 30.8 23.4 17.5 12.88 87.7 66.6 51.6 40.4 31.9 25.1 19.6

Marcellus play

6 6.5 7 7.5 8 8.5 9 9.5 102 -15.2 -16.3 -17.2 -18.0 -18.8 -19.4 -20.0 -20.6 -21.13 -7.9 -9.6 -11.0 -12.3 -13.3 -14.3 -15.1 -15.9 -16.64 -0.2 -2.6 -4.6 -6.4 -7.9 -9.2 -10.3 -11.3 -12.25 8.0 4.8 2.1 -0.2 -2.1 -3.8 -5.3 -6.7 -7.86 16.5 12.5 9.2 6.3 3.9 1.7 -0.1 -1.8 -3.37 25.3 20.5 16.5 13.1 10.1 7.5 5.3 3.2 1.58 34.2 28.7 24.0 20.0 16.5 13.5 10.8 8.5 6.4

Haynesville play

5 5.5 6 6.5 7 7.5 8 8.5 92 -15.5 -16.8 -18.0 -18.7 -19.5 -20.2 -20.8 -21.3 -21.83 -9.0 -10.7 -12.0 -13.5 -14.6 -15.5 -16.4 -17.0 -17.84 -2.0 -4.5 -6.6 -8.3 -9.8 -11.0 -12.2 -13.2 -14.05 5.3 2.0 -0.7 -3.0 -4.9 -6.6 -8.0 -9.2 -10.36 13.0 8.8 5.4 2.5 0.1 -1.9 -3.7 -5.2 -6.57 21.0 15.7 11.6 8.2 5.4 2.9 0.8 -1.1 -2.78 28.6 22.8 18.1 14.1 10.7 7.9 5.4 3.2 1.3

Montney play

71

5 5.5 6 6.5 7 7.52 -17.0 -18 -19 -20 -20.6 -21.33 -10.6 -12.3 -13.7 -15 -16 -16.94 -4.2 -6.5 -8.5 -10 -11.5 -12.75 2.6 -0.5 3.1 -5.2 -7 -8.56 9.7 5.8 2.6 0 -2.2 -4.27 17.0 12.3 8.5 5.3 2.6 0.38 24.5 19 14.6 10.8 7.7 5

Utica play

(Gas Price , CAP_EX ) Payout period (years) Sensitivity Analysis (P50 profiles):

2.5 3 3.5 4 4.52 NA NA NA NA NA3 NA NA NA NA NA4 4.0 8.0 NA NA NA5 2.5 3.5 5.5 10.0 NA6 1.8 2.0 3.0 5.0 8.57 1.2 1.8 2.2 3.0 4.08 1.0 1.5 2.0 2.2 3.0

CAP_EX ($million)

Gas Price ($/m

cf)

Barnett play

2.5 3 3.5 4 4.5 5 5.52 NA NA NA NA NA NA NA3 3.5 7 NA NA NA NA NA4 2.0 3 4 7 NA NA NA5 1.5 2 2.5 3.2 4.5 7 NA6 1 1.2 1.8 2 3 3.8 4.57 <1 1 1.2 1.5 2 2.5 3.28 <1 <1 1 1.2 1.8 2 2.2

Marcellus play

72

6 6.5 7 7.5 8 8.5 9 9.5 102 NA NA NA NA NA NA NA NA NA3 NA NA NA NA NA NA NA NA NA4 NA NA NA NA NA NA NA NA NA5 4.2 5.5 9.0 NA NA NA NA NA NA6 2.5 3.0 4.0 4.5 6.0 9.0 NA NA NA7 1.8 2.0 2.5 3.0 3.5 4.5 5.0 6.5 8.58 1.0 1.5 1.8 2.0 2.5 2.8 3.0 4.0 5.0

Haynesville play

5 5.5 6 6.5 7 7.5 8 8.5 92 NA NA NA NA NA NA NA NA NA3 NA NA NA NA NA NA NA NA NA4 NA NA NA NA NA NA NA NA NA5 5.5 8.5 NA NA NA NA NA NA NA6 3 4 5 7 >10 NA NA NA NA7 2.0 3 3.5 4 5.5 7 >10 NA NA8 1.8 2 2.5 3 3.5 4.5 5 6.5 >10

Montney play

5 5.5 6 6.5 7 7.52 NA NA NA NA NA NA3 NA NA NA NA NA NA4 NA NA NA NA NA NA5 8.0 NA NA NA NA NA6 4 5 8 NA NA NA7 2.5 3 4 5 8 >108 2.0 2.2 3 3.5 4.5 5.5

Utica play

(Gas Price , CAP_EX ) NPV (10%, $million) Sensitivity Analysis (P90 profiles):

73

2.5 3 3.5 4 4.52 -1.6 -2 -2.6 -3 -3.63 -1.1 -1.6 -2.1 -2.6 -3.14 -0.7 -1.1 -1.7 -2.2 -2.65 -0.2 -0.7 -1.2 -1.7 -2.26 0.2 -0.3 -0.7 -1.2 -1.77 0.7 0.2 -0.3 -0.8 -1.28 1.2 0.7 0.2 -0.3 -0.8

Barnett playCAP_EX ($million)

Gas Price ($/m

cf)

2.5 3 3.5 4 4.5 5 5.52 -0.8 -1.3 -1.8 -2.3 -2.9 -3.3 -3.83 -0.04 -0.5 -1.0 -1.5 -2.0 -2.5 -3.04 0.7 0.3 -0.2 -0.7 -1.2 -1.7 -2.25 1.6 1.0 0.6 0.1 -0.4 -0.9 -1.46 2.4 1.9 1.4 0.9 0.4 -0.1 -0.57 3.3 2.7 2.2 1.7 1.2 0.7 0.28 4.0 3.5 3.0 2.6 2.0 1.6 1.0

Marcellus play

6 6.5 7 7.5 8 8.5 9 9.5 102 -3.8 -4.4 -4.8 -5.3 -5.8 -6.3 -6.8 -7.3 -7.83 -2.7 -3.2 -3.7 -4.2 -4.7 -5.2 -5.7 -6.2 -6.74 -1.60 -2.1 -2.6 -3.1 -3.6 -4.1 -4.6 -5.1 -5.65 -0.5 -1.0 -1.5 -2.00 -2.5 -3.0 -3.5 -4.0 -4.66 0.5 0.0 -0.5 -1.0 -1.5 -2.0 -2.40 -3.0 -3.47 1.6 1.1 0.6 0.1 -0.4 -0.9 -1.4 -1.9 -2.48 2.6 2.2 1.7 1.2 0.7 0.2 -0.3 -0.8 -1.3

Haynesville play

74

5 5.5 6 6.5 7 7.5 8 8.5 92 -3.4 -3.9 -4.4 -4.9 -5.4 -5.9 -6.5 -6.9 -7.43 -2.6 -3.1 -3.6 -3.7 -4.6 -5 -5.3 -5.8 -6.64 -2.0 -2.3 -2.8 -3 -3.8 -4.2 -4.8 -5.5 -5.95 -1.0 -1.6 -2 -2.2 -2.7 -3.3 -3.7 -4.4 -56 -0.3 -0.8 -0.3 -1.8 -2.3 -2.9 -3.4 -3.9 -4.37 0.5 -0.03 -0.5 -1.3 -1.5 -1.9 -2.4 -3 -3.58 1.2 0.7 0.2 -0.2 -0.7 -1.3 -1.9 -2.3 -2.7

Montney play

5 5.5 6 6.5 7 7.52 -3.9 -4.4 -4.9 -5.4 -5.9 -6.43 -3.3 -3.9 -4.3 -4.9 -5.3 -5.84 -2.8 -3.3 -3.8 -4.3 -4.8 -5.35 -2.3 -2.8 -3.3 -3.8 -4.3 -4.86 -1.7 -2.2 -2.7 -3.2 -3.7 -4.27 -1.2 -1.7 -2.2 -2.7 -3.2 -3.78 -0.6 -1.2 -1.6 -2.2 -2.7 -3.1

Utica play

(Gas Price , CAP_EX ) IRR (NPV = 0, %) Sensitivity Analysis (P90 profiles):

2.5 3 3.5 4 4.52 -18.5 -20.4 -21.8 -23.0 -23.83 -13.2 -16.0 -17.7 -19.2 -20.34 -7.9 -11.4 -14 -15.8 -17.35 -2.4 -7.0 -10.1 -12.5 -14.36 3.3 -2.4 -6.3 -9.2 -11.47 9.2 2.3 -2.4 -6.0 -8.48 15.2 7.2 1.7 -2.4 -5.3

CAP_EX ($million)

Gas Price ($/m

cf)

Barnett play

75

2.5 3 3.5 4 4.5 5 5.52 -10.5 -13.6 -15.9 -17.6 -19.0 -20.0 -21.03 -0.5 -5.6 -9.0 -11.6 -13.6 -15.2 -16.54 10.0 2.9 -2.0 -5.6 -8.3 -10.4 -12.25 21.2 11.9 5.5 0.8 -2.8 -5.6 -7.86 32.7 21.3 13.3 7.4 3.0 -0.5 -3.37 44.3 30.8 21.3 14.3 9.0 4.7 1.48 56.1 40.4 29.4 21.3 15.0 10.0 6.2

Marcellus play

6 6.5 7 7.5 8 8.5 9 9.5 102 -19.1 -20.0 -20.7 -21.4 -21.9 -22.5 -23.0 -23.4 -23.83 -13.8 -15.0 -16.0 -16.9 -17.7 -18.4 -19.1 -19.7 -20.24 -8.50 -10.2 -11.6 -12.7 -13.8 -14.7 -15.5 -16.3 -16.95 -3.0 -5.2 -7.0 -8.50 -9.8 -11.0 -12.0 -13.0 -13.86 2.8 0.1 -2.2 -4.1 -5.8 -7.5 -8.50 -9.6 -10.67 8.8 5.5 2.8 0.4 -1.6 -3.3 -4.8 -6.2 -7.48 15.0 11.1 7.9 5.2 2.8 0.7 -1.1 -2.7 -4.1

Haynesville play

5 5.5 6 6.5 7 7.5 8 8.5 92 -20.2 -21.0 -21.8 -22.5 -23.1 -24.5 -25.1 -25.9 -26.73 -15.5 -16.7 -18.0 -19.0 -19.4 -19.8 -20.4 -21.1 -21.84 -11.0 -12.7 -14.0 -15.2 -16.1 -16.6 -17.5 -18.2 -19.15 -6.6 -8.6 -10.3 -12.5 -13.0 -14.2 -15.0 -15.9 -16.56 -2.0 -4.5 -6.5 -8.3 -9.8 -10.5 -11.0 -12.8 -14.07 3.0 -0.2 -2.7 -4.8 -6.5 -7.6 -9.0 -10.2 -11.58 8.0 4.2 1.3 -1.2 -3.2 -5.2 -6.7 -8.0 -9.1

Montney play

76

5 5.5 6 6.5 7 7.52 -23.7 NA NA NA NA NA3 -20.1 -21.0 -21.8 -22.5 -23.1 -23.74 -17.0 -18.0 -19.0 -19.8 -20.6 -21.25 -13.7 -15.0 -16.3 -17.3 -18.2 -19.06 -10.6 -12.3 -13.7 -15.0 -16.0 -17.07 -7.4 -9.4 -11.1 -12.5 -13.7 -14.78 -4.1 -6.5 -8.5 -10.0 -11.5 -12.7

Utica play

(Gas Price , CAP_EX ) Payout period (years) Sensitivity Analysis (P90 profiles):

2.5 3 3.5 4 4.52 NA NA NA NA NA3 NA NA NA NA NA4 NA NA NA NA NA5 NA NA NA NA NA6 7.0 NA NA NA NA7 4.0 8.0 NA NA NA8 3.0 4.5 9.0 NA NA

CAP_EX ($million)

Gas Price ($/m

cf)

Barnett play

2.5 3 3.5 4 4.5 5 5.52 NA NA NA NA NA NA NA3 NA NA NA NA NA NA NA4 3.5 7 NA NA NA NA NA5 2.0 3.5 5 10 NA NA NA6 1.5 2 3 4 7 NA NA7 1.2 1.8 2 3 4 5.5 88 1.0 1.5 1.8 2 3 3.5 5

Marcellus play

77

6 6.5 7 7.5 8 8.5 9 9.5 102 NA NA NA NA NA NA NA NA NA3 NA NA NA NA NA NA NA NA NA4 NA NA NA NA NA NA NA NA NA5 NA NA NA NA NA NA NA NA NA6 7.0 >10 NA NA NA NA NA NA NA7 4.0 5.0 7.0 >10 NA NA NA NA NA8 3.0 3.5 4.0 5.5 7.0 >10 NA NA NA

Haynesville play

5 5.5 6 6.5 7 7.5 8 8.5 92 NA NA NA NA NA NA NA NA NA3 NA NA NA NA NA NA NA NA NA4 NA NA NA NA NA NA NA NA NA5 NA NA NA NA NA NA NA NA NA6 NA NA NA NA NA NA NA NA NA7 7.0 NA NA NA NA NA NA NA NA8 4.5 6 9.5 NA NA NA NA NA NA

Montney play

5 5.5 6 6.5 7 7.52 NA NA NA NA NA NA3 NA NA NA NA NA NA4 NA NA NA NA NA NA5 NA NA NA NA NA NA6 NA NA NA NA NA NA7 NA NA NA NA NA NA8 NA NA NA NA NA NA

Utica play

(Gas Price , IP rate ) NPV (10%, $million) Sensitivity Analysis:

78

2 2.5 3 3.5 4 4.5 52 -2.6 -2.4 -2.1 -1.9 -1.6 -1.4 -1.23 -2.1 -1.7 -1.4 -1.0 -0.7 -0.4 -0.14 -1.6 -1.1 -0.7 -0.2 0.2 0.6 1.15 -1.2 -0.6 0.0 0.5 1.1 1.7 2.26 -0.7 0.0 0.6 1.3 2.0 2.7 3.47 -0.2 0.5 1.3 2.1 2.9 3.7 4.58 0.2 1.1 2.0 2.9 3.8 4.8 5.7

Barnett playIP rate (Mmcf/d)

Gas Price ($/m

cf)

2.5 3 3.5 4 4.5 52 -2.6 -2.3 -2.0 -1.8 -1.5 -1.33 -2.0 -1.5 -1.1 -0.7 -0.3 0.14 -1.3 -0.7 -0.2 0.4 0.9 1.45 -0.5 0.1 0.8 1.4 2.1 2.86 0.1 0.9 1.7 2.5 3.4 4.27 0.7 1.7 2.7 3.6 4.6 5.58 1.5 2.5 3.6 4.7 5.9 7.0

Marcellus play

8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 13.5 142 -6.9 -6.8 -6.7 -6.6 -6.5 -6.4 -6.3 -6.2 -6.1 -6 -5.9 -5.8 -5.73 -6.0 -5.9 -5.7 -5.5 -5.3 -5.1 -4.9 -4.85 -4.8 -4.7 -4.5 -4.4 -4.24 -5.2 -5 -4.8 -4.6 -4.4 -4.2 -4 -3.8 -3.6 -3.44 -3.2 -3 -2.85 -4.4 -4.1 -3.9 -3.6 -3.3 -3 -2.7 -2.5 -2.4 -2.2 -1.9 -1.7 -1.56 -3.6 -3.3 -3 -2.7 -2.4 -2.1 -1.8 -1.5 -1.2 -0.9 -0.6 -0.3 0.017 -2.8 -2.4 -2.1 -1.7 -1.4 -1 -0.7 -0.3 0.02 0.4 0.7 1 1.48 -2.0 -1.6 -1.2 -0.7 -0.3 0.02 0.4 0.8 1.2 1.6 2 2.5 2.8

Haynesville play

3 3.5 4 4.5 5 5.5 62 -5.4 -5.1 -4.9 -4.6 -4.4 -4.1 -3.93 -4.6 -4.2 -3.8 -3.5 -3.1 -2.7 -2.34 -3.8 -3.3 -2.8 -2.3 -1.8 -1.3 -0.75 -3.0 -2.4 -1.8 -1.1 -0.4 0.1 0.86 -2.3 -1.5 -0.7 0.0 0.8 1.6 2.37 -1.5 -0.6 0.3 1.2 2.1 3.0 3.98 -0.7 0.3 1.3 2.3 3.4 4.4 5.5

Montney play

79

2 2.5 3 3.5 4 4.5 5 5.5 62 -4.2 -3.8 -3.3 -2.9 -2.4 -2 -1.6 -1.1 -0.73 -3.4 -2.7 -2 -1.3 -0.7 -0.01 0.6 1.2 1.94 -2.4 -1.6 -0.7 0.2 1 2 2.8 3.7 4.55 -1.5 -0.4 0.6 1.7 2.8 3.9 5 6.1 7.26 -0.7 0.6 1.9 3.2 4.5 5.9 7.2 8.5 9.87 0.2 1.7 3.2 4.8 6.3 7.8 9.4 10.9 12.58 1 2.8 4.5 6.3 8.1 9.8 11.6 13.4 15.1

Utica play

(Gas Price , IP rate ) IRR (NPV = 0, %) Sensitivity Analysis:

2 2.5 3 3.5 4 4.5 52 -21.8 -19.7 -17.7 -15.8 -14 -12.1 -103 -17.7 -14.8 -12 -9.2 -6.4 -3.4 -0.44 -13.9 -10.2 -6.3 -2.4 1.6 5.8 105 -10.0 -5.4 -0.4 4.70 10.0 15.4 21.96 -6.3 -0.4 5.8 12.2 18.7 25.4 32.27 -2.4 4.8 12.2 19.9 27.7 35.6 43.68 1.7 10 18.8 27.7 36.7 45.9 55.1

Barnett playIP rate (Mmcf/d)

Gas Price ($/m

cf)

2.5 3 3.5 4 4.5 52 -19.6 -17.6 -15.6 -13.6 -11.7 -9.73 -14.6 -11.6 -8.6 -5.6 -2.5 0.74 -9.6 -5.6 -1.4 2.9 7.4 12.05 -4.5 0.8 6.3 12.0 17.7 23.66 0.8 7.4 14.2 21.3 28.4 35.67 6.3 14.3 22.4 30.8 39.2 47.78 12.0 21.3 30.8 40.4 50.2 60.0

Marcellus play

80

8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 13.5 142 -24.0 -23.7 -23.2 -22.8 -22.4 -22.0 -21.6 -21.2 -20.8 -20.4 -20.0 -19.7 -19.33 -21.0 -20.2 -19.6 -19.1 -18.5 -18.0 -17.5 -17.0 -16.5 -16.0 -15.5 -15.0 -14.54 -18.0 -17.1 -16.4 -15.8 -15.1 -14.4 -13.7 -13.1 -12.4 -11.7 -11.1 -10.4 -9.75 -15.1 -14.2 -13.4 -12.5 -11.7 -10.9 -10.0 -9.2 -8.4 -7.5 -6.7 -5.8 -4.96 -12.4 -11.4 -10.4 -9.3 -8.3 -7.3 -6.3 -5.3 -4.2 -3.2 -2.1 -1.0 0.07 -9.7 -8.5 -7.3 -6.1 -4.9 -3.7 -2.4 -1.2 0.1 1.3 2.6 3.9 5.28 -6.9 -5.6 -4.2 -2.8 -1.4 0.1 1.5 3.0 4.5 6.0 7.5 9.0 10.5

Haynesville play

3 3.5 4 4.5 5 5.5 62 -23.1 -21.8 -20.6 -19.5 -18.4 -17.3 -16.23 -19.4 -17.8 -16.2 -14.6 -13 -11.4 -9.84 -16.1 -14 -12 -9.8 -7.7 -5.5 -3.35 -13.0 -10.3 -7.6 -5 -2.1 0.7 3.66 -9.8 -6.5 -3.2 0.1 3.6 7.1 10.77 -6.5 -2.7 1.3 5.4 9.5 13.8 18.18 -3.2 1.3 6 10.7 15.6 20.6 25.6

Montney play

2 2.5 3 3.5 4 4.5 5 5.5 62 -15 -13 -12 -10 -8.8 -7 -5.6 -4 -2.53 -12 -9.5 -7 -5 -2.5 -0.2 2.1 4.4 6.74 -8.7 -5.6 -2.5 0.6 3.6 6.7 10 13.1 16.35 -5.5 -1.7 2.0 6 10 14 18 22 26.26 -2.5 2.1 7 11.5 16.3 21 26 31 367 0.6 6 11.5 17.1 23 29 35 40 478 3.7 10 16 23 30 36 43 50 58

Utica play

(Gas Price , IP rate ) Payout period (years) Sensitivity Analysis:

81

2 2.5 3 3.5 4 4.5 52 NA NA NA NA NA NA NA3 NA NA NA NA NA NA NA4 NA NA NA NA 9 5.5 45 NA NA NA 5.5 4 2.8 26 NA NA 5.5 3 2.5 2 1.57 NA 5.5 3 2.2 2 1.5 18 10 4 2.5 2 1.5 1 <1

IP rate (Mmcf/d)

Gas Price ($/m

cf)

Barnett play

2.5 3 3.5 4 4.5 52 NA NA NA NA NA NA3 NA NA NA NA NA 104 NA NA NA 7 4.5 35 NA 10 5 3.1 2.5 26 10 4.5 3 2 1.9 1.57 5 3 2 1.8 1.2 18 3.0 2 1.5 1.2 1 <1

Marcellus play

8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 13.5 142 NA NA NA NA NA NA NA NA NA NA NA NA NA3 NA NA NA NA NA NA NA NA NA NA NA NA NA4 NA NA NA NA NA NA NA NA NA NA NA NA NA5 NA NA NA NA NA NA NA NA NA NA NA NA NA6 NA NA NA NA NA NA NA NA NA NA NA NA 107 NA NA NA NA NA NA NA NA >10 10 8 7 58 NA NA NA NA NA >10 10 8.5 6 5.5 5 4 3.5

Haynesville play

3 3.5 4 4.5 5 5.5 62 NA NA NA NA NA NA NA3 NA NA NA NA NA NA NA4 NA NA NA NA NA NA NA5 NA NA NA NA NA >10 6.56 NA NA NA >10 7 4.5 3.57 NA NA 10 5.5 4 3 2.58 NA 10 5 3.5 2.8 2 <2

Montney play

82

2 2.5 3 3.5 4 4.5 5 5.5 62 NA NA NA NA NA NA NA NA NA3 NA NA NA NA NA NA >10 8 6.54 NA NA NA >10 9 6 5 4 35 NA NA >10 6 5 4 3 2.5 26 NA >10 6 4 >3 3 2 <2 1.57 >10 6.5 4 3 <3 2 <2 1.5 18 9 5 3 2.5 2 <2 1.5 1 <1

Utica play

The impact of FYGP on P50 NPV (Gas Price , CAP_EX ) project economics

2.5 3 3.5 4 4.52 0.80 0.35 -0.15 -0.60 -1.103 1.30 0.87 0.40 -0.12 -0.604 1.80 1.39 0.89 0.39 -0.105 2.40 1.90 1.40 0.90 0.406 2.90 2.42 1.90 1.40 0.907 3.43 2.93 2.43 1.93 1.408 3.95 3.45 2.90 2.45 1.90

CAP_EX ($million)

Gas Price ($/m

cf)

FYGP = 8$/mcf; Rest of the years 2 ≤ Gas Price ≤ 8$/mcf

Barnett play

2.5 3 3.5 4 4.5 5 5.52 2.1 1.6 1.1 0.6 0.1 -0.4 -0.93 2.8 2.3 1.8 1.3 0.8 0.3 -0.24 3.5 2.9 2.5 1.9 1.5 0.9 0.55 4.1 3.7 3.1 2.6 2.1 1.6 1.16 4.8 4.3 3.9 3.3 2.8 2.3 1.97 5.5 5.1 4.5 4.0 3.5 3.0 2.68 6.2 5.7 5.2 4.8 4.2 3.7 3.2

Marcellus play

83

6 6.5 7 7.5 8 8.5 9 9.5 102 0.4 -0.1 -0.6 -1.1 -1.6 -2.1 -2.6 -3.1 -3.53 1.3 0.8 0.3 -0.2 -0.7 -1.2 -1.7 -2.2 -2.74 2.1 1.7 1.2 0.7 0.2 -0.2 -0.8 -1.3 -1.75 3.1 2.7 2.2 1.7 1.1 0.6 0.2 -0.3 -0.86 4.1 3.5 3.0 2.6 2.0 1.6 1.0 0.6 0.07 5.0 4.5 4.0 3.4 3.0 2.5 2.0 1.6 1.08 6.0 5.4 4.9 4.5 4.0 3.4 2.9 2.4 2.0

Haynesville play

5 5.5 6 6.5 7 7.5 8 8.5 92 -0.1 -0.6 -1.1 -1.7 -2.1 -2.7 -3.1 -3.6 -4.13 0.6 0.1 -0.4 -0.9 -1.4 -1.8 -2.4 -2.9 -3.44 1.3 0.9 0.4 -0.1 -0.6 -1.1 -1.7 -2.1 -2.65 2.1 1.6 1.1 0.6 0.2 -0.4 -0.9 -1.3 -1.96 2.8 2.4 1.9 1.4 1.0 0.4 0.0 -0.6 -1.17 3.8 3.1 2.7 2.0 1.6 1.2 0.6 0.1 -0.38 4.4 3.9 3.4 2.8 3.4 1.8 1.5 1.0 0.5

Montney play

5 5.5 6 6.5 7 7.52 -0.4 -0.9 -1.4 -2.0 -2.4 -2.93 0.3 -0.3 -0.7 -1.3 -1.7 -2.34 0.9 0.5 0.0 -0.6 -1.0 -1.65 1.6 1.1 0.6 0.1 -0.4 -0.96 2.3 1.9 1.3 0.8 0.4 -0.27 3.0 2.5 1.9 1.5 1.1 0.58 3.6 3.1 2.7 2.2 1.8 1.1

Utica play

84

2.5 3 3.5 4 4.52 -0.60 -1.10 -1.60 -2.10 -2.603 -0.08 -0.50 -1.00 -1.50 -2.004 0.40 -0.07 -0.60 -1.00 -1.505 0.94 0.40 -0.05 -0.50 -1.006 1.40 1.00 0.46 -0.03 -0.507 1.90 1.40 0.97 0.50 -0.028 2.49 1.99 1.50 1.00 0.50

The impact of FYGP on P50 NPV (Gas Price , CAP_EX ) project economicsFYGP = 3$/mcf; Rest of the years 2 ≤ Gas Price ≤ 8$/mcf

Barnett playCAP_EX ($million)

Gas Price ($/m

cf)

2.5 3 3.5 4 4.5 5 5.52 0.1 -0.4 -0.9 -1.4 -1.9 -2.4 -2.83 0.7 0.2 -0.2 -0.7 -1.2 -1.7 -2.24 1.4 0.9 0.5 0.0 -0.5 -1.0 -1.55 2.1 1.7 1.1 0.7 0.2 -0.3 -0.86 2.8 2.3 1.8 1.3 0.8 0.3 -0.17 3.5 3.0 2.6 2.1 1.5 1.0 0.58 4.2 3.7 3.2 2.8 2.2 1.7 1.2

6 6.5 7 7.5 8 8.5 9 9.5 102 -2.5 -2.9 -3.6 -4.0 -4.4 -5.0 -5.5 -6.0 -6.43 -1.5 -2.0 -2.6 -3.1 -3.5 -4.0 -4.5 -5.0 -5.54 -0.6 -1.1 -1.6 -2.1 -2.7 -3.1 -3.6 -4.0 -4.65 0.3 -0.1 -0.6 -1.2 -1.7 -2.1 -2.6 -3.2 -3.66 1.4 0.8 0.2 -0.2 -0.7 -1.3 -1.7 -2.1 -2.87 2.2 1.7 1.2 0.7 0.2 -0.3 -0.8 -1.3 -1.88 3.1 2.6 2.1 1.7 1.1 0.6 0.2 -0.5 -0.9

Marcellus play

Haynesville play

5 5.5 6 6.5 7 7.5 8 8.5 92 -2.2 -2.7 -3.3 -3.6 -4.3 -4.7 -5.2 -5.8 -6.33 -1.5 -1.9 -2.5 -3.0 -3.5 -3.9 -4.5 -5.0 -5.64 -0.7 -1.3 -1.6 -2.2 -2.7 -3.2 -3.6 -4.3 -4.75 0.0 -0.5 -0.9 -1.5 -2.0 -2.4 -2.9 -3.5 -4.16 0.7 0.4 -0.2 -0.7 -1.2 -1.7 -2.2 -2.6 -3.27 1.5 1.0 0.6 0.0 -0.4 -1.0 -1.5 -1.9 -2.58 2.3 1.7 1.3 0.8 0.4 -0.2 -0.7 -1.3 -1.7

Montney play

85

5 5.5 6 6.5 7 7.52 -2.3 -2.8 -3.5 -3.9 -4.4 -53 -1.8 -2.3 -2.6 -3.2 -3.8 -4.24 -1.0 -1.6 -2 -2.5 -3 -3.65 -0.4 -0.9 -1.4 -1.8 -2.3 -2.96 0.3 0.0 -0.8 -1.3 -1.7 -2.27 1 0.5 0 -0.5 -1 -1.68 1.7 1.1 0.7 0.2 -0.4 -0.9

Utica play

86

A.4. Type Curves under 3 different production scenarios (a), (b), (c), (d), and (e) represent cumulative production curves (EURs (bcf) on the y axis versus time (we only consider the first 10 years of the total life span of wells) on the x axis) for Haynesville, Utica, Barnett, Marcellus, and Montney P10, P50 and P90 wells, respectively.

(a)

(b)

(c)

87

(d)

(e)

88

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