Tight oil production and the industry outlook in the Bakken Formation

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M.Sc. International Economics and Business - University of Groningen

M.A. International Economy and Business - Corvinus University of Budapest

Double Degree Program

Master Thesis July 2013

Tight oil production and the industry

outlook in the Bakken Formation

Adam Holczer

Student number: S-2419556

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Abstract

Over the last decade a huge upsurge could have been observed in the tight oil production from the Bakken Formation, which has several impacts on the US and the global industry as well. As extracting from shale plays is in an early stage, understanding the factors of production and the players can be crucial. Using panel data across 29 oil fields in Montana based on the technological view of oil production can give a basic understanding why the boom could have happened in the 2000s. Focusing on the market outlook, it outlines the fundamental role of small independents whose presence was inevitable in the increase of tight oil production. The results confirm the positive indirect effect of oil price on production and on the presence of companies, especially in case of small independents.

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I. Introduction

In the last decade the “shale revolution” could have been observed in the United States, regardless of the fact whether it is natural gas or crude oil. A tremendous consequence of the shale production boom could be observed, which changed the US domestic energy mix and the outlook of the global energy industry as well (Luciani, 2013). It started with the shale gas boom at the turn of the century, whereas in 2000 shale gas production gave only 2 % of the total American natural gas production, while in 2010 23 % was given from shale gas production (EIA, 2013a). This trend is similar for the flourishing shale oil production, where in 2000 barely 3% of the total oil production, while in 2010 almost 15 % was coming from tight oil fields (EIA, 2013a). In 2012 March, production from tight oil plays approached 900 000 barrels of oil per day (bbl/d), from which Bakken Formation gave a massive amount (Figure 1. – Appendix). Although this immense increase in production may not make the United States net exporter, huge change occurred and is occurring. There is a mixture of economic, technological and political reasons for why it happened in the US and which factors triggered this upsurge in the beginning of the new millennium (Gordon, 2012). In this analysis these factors are presented through the examination of the tight oil production in the Bakken Formation in Montana with a special focus on the effect of recent high oil prices that resulted in the application of techniques that can make oil production commercially viable from sites what was previously seen uneconomic or challenging to extract (IEA, 2011). My intention is to reveal these factors behind the increasing tight oil production and shed light on the early industry structure regarding the presence of small independent1 companies.

The Bakken Formation is in the Williston Basin, under the states of North Dakota and Montana and it stretches over the border till the middle of Alberta, Canada. Recent assessment reports have been measured the formation as one of the largest tight oil play within the US and the world (EIA, 2013b; Gaswirth et al., 2013). The formation consists of numerous fields, each with heterogeneous production numbers, but with the same type of oil: light tight oil (Pollastro et al., 2011). Tight oil refers to conventional crude oil trapped within unconventional conditions, low permeability reservoirs deep below the surface. In order to liberate these resources, assistance from technologically advanced stimulation treatments is needed (CSUR, 2012). Due to the characteristics of tight oil, the extraction process requires new technology i.e. the combination of horizontal drilling and multi-stage hydraulic

1_{In this study, the firms have been categorized by the number of the employees, created two categories. Small}

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fracturing, that makes the production more expensive and complex (Helms, 2008). Although each method has been used for decades separately, the combination of these was a major breakthrough in the late 90’s and fundamentally shook the unconventional oil industry.

With the US production of tight oil, a more than a two and a half decade-long downward trend has been turned into upsurge in the 2000s which had a serious impact on the US energy import as well (Sieminski, 2013; Stevens, 2010). The tight oil production is the largest driver of incremental US oil production, with the projected volume of 1.36 million bbl/d in 2016, where 55 % will come from the Bakken Forrmation alone (IEA, 2011). As US is the biggest consumer of petroleum (Hirsch et al., 2005) the demand alteration can undermine the position of traditionally big oil exporters through import decrease (Carter, 2013). The US petroleum import had already started to decline and projected to decrease further strongly mostly due to the tight oil production (EIA, 2012). Among others these facts, with the rising demand from the economies in the Far East and the pressure to change environmentally less harmful resources, are pinpointing into a direction where rising US shale production plays an exceptional role.

The initial aim of the thesis is to provide answers and evidence on the economic side of production and shed light on the connection between prices and production. On the other hand, the study tries to unveil the causes of the high presence of small companies.

In the first part, the basic theoretical starting points are described that are needed to understand the direction of the analysis and the hypothesis. After discussing the hypothesis a short literature review is presented with the researches that dealt with modeling oil production. Then the methodology part, the model specification and data collection process are defined and the results are given. Finally a summary of the results and conclusion takes place.

II. Theory

As far as the oil production is concerned, there are two main perceptions regarding the present and the future oil production; the geological view and the technological/economic view (Horne, 2008). Although these concepts are overlapping, the initial points differ slightly. In order to control for both theories in the analysis, a mixture of the concept is used during the analysis.

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of oil production (Hubbert, 1962; Deffeyes, 2001; Campbell and Laherrére, 1998). Basically the proponents of this concept support the ‘Peak Oil’ theory (Horne, 2008). It starts with the fact that the initial resource quantity limits the production, regardless of the level of prices, costs and any economic factors. These geological facts seemed to be strict and overriding, which was reflected in this analysis as well, as regardless of the level of oil prices, the production is fundamentally determined by natural depletion.

However, the role of crude oil, its price and its production in the world economy is undisputed. Regardless of conventional or non-conventional oil, oil prices impact economies (Hamilton, 2000; Blanchard and Galí, 2007; Rasmussen and Roitman, 2011; OECD, 2011; Reynolds, 2005), oil production and technological use (Moroney and Berg, 1999; Benes et al., 2012; Kavand and Shahmoradi, 2009). Although it is fossil non-renewable resource, the quantity of reserves is in connection with the level of prices as the evaluation of technically and/or economically recoverable reserves is calculated on the basis of current oil price. The

economic or technological view argues that higher oil prices have effect on oil production

through encouraging new technological solutions (Hamilton, 2009; Adelman, 2004; Maugeri, 2009) that ends up in higher volume of extraction. It is called the ‘business as usual’ concept also (Horne, 2008). That happened according to many experts; the rising oil prices from the beginning of the decade were a crucial indirect element that triggered tight oil production (Verrastro, 2012; Hahn and Passel, 2010; LeFever and Helms, 2006; Maugeri, 2012). It can be considered as an indirect element, as due to the price increase, new technologies became commercially viable and cost-effective, which eventually caused a surge in production (Grape, 2006; Maugeri, 2012; Lewis, 2006; Medlock, 2012). This theory manifests itself and becomes clear if we approach from a company’s perspective. If the prices go up, the mark-up ratio incentivizes new exploration and drilling activities. Therefore the production numbers will rise, as previously uneconomic sites are involved into production (Heun and de Wit, 2012). Strong evidence can be found from the late 70s till the middle of the 80s, where as a result of the rising prices, domestic production rose and previously commercially non-viable leases became reviewed and experimenting with new techniques took place (Reiss, 1990). The case could be similar in the Bakken Formation, as it is a strongly technology-driven shale play (Theloy and Sonnenberg, 2013).

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shale reservoirs tend to be thin and extensive, long laterals and multi-stage fracturing are necessary, in order to exploit the hydrocarbon economically (Gaswirth et al., 2013; McGlade, 2012). As the surge in the production in Bakken can be traced back to the oil price increase, the technological view forms the base of the hypotheses (described later). Beside the theoretical extraction methods, relying upon non-official information from the North Dakota Department of Mineral Resources, hydraulic fracturing is used indeed in case of the majority (considerably more than 90 %) of the horizontally drilled wells.

If the economic perspective is analyzed, the cost of producing oil has to be addressed. In case of tight oil, it is clearly costlier and more complex as it is described. Therefore an average of 50 to 65 $/bbl is indicated as a minimum price level in order to extract the tight oil resource profitably (Maugeri, 2012). Further studies show an average breakeven price of 65-70 $ per barrel of light tight oil (IEA, 2013); while another estimate states that the cost of development is about 50 $ per barrel which makes tight oil more competitive than Canadian tar sand and ultra-deep offshore oil production (IEA, 2011). It is concluded that production is highly dependent on the level of prices, although that does not mean that production will be entirely suspended below a specific rate. It only strengthens the argument stating that the tight oil production is very responsive to prices and increase sharply at high oil prices (or declines steeply in case of low oil prices) (Staub, 2012). In this case tight oil production could create a “band” for an additional production above a specific level of oil price (Luciani, 2013).

It must be acknowledged that there is a great variety of influencing factors on oil production and it is difficult to say which one of them is the most important (Theloy and Sonnenberg, 2013). Nevertheless Bakken is a highly technology-driven play, meaning that drilling technique and technology in general play a key role. In addition recent studies show that the economic part plays an outstanding role and needs to be analyzed more deeply (Sorrell et al., 2009; Sorrell et al., 2010).

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acquisitions took place (i.e. ExxonMobil, StatOil). There are numerous reasons that lead to the current characteristics of the industry; these causes are described in the following section.

1. Industry outlook

Before diving into the industry details, an attentive look should be taken onto the map as geography (and geology as well) plays an outstanding role regarding the cost structure and the characteristics of the industry. The location of oil-prolific areas determines the exploration, extraction and the production costs, which can fundamentally impact the size and characteristics of operating companies eventually. Beyond the geographical factors, the political, economic and geopolitical factors can affect the scale of operating companies and since most of the proven reserves are in countries with non-democratic and/or unstable political system (IEA, 2008), the history of oil industry is characterized by large firms with huge financial power (Sampson, 1975). Three theoretical categories have been created with the intention of understanding the high presence of small(er) firms in the Bakken and of revealing the factors that affect the most the cost structure of specific areas.

In the first group there are countries with very low production cost, but with difficult accessibility to resources due to entry barriers, national legislation, resource nationalism, and high starting fixed cost (Sorrell et al., 2009). Majors and super majors are assumed to characterize this part of the industry (Ernst & Young, 2012). These countries are the MENA (Middle East and North Africa) countries, and Nigeria, Venezuela etc.

In the second group, the extraction locations have been set which demand huge technological and financial preparedness, i.e. ultra-deep water drilling sites, areas with harsh environment etc. These locations (Siberia, Arctic ice, offshore America) require such technological advancement (Russia Today, 2012; Canty, 2010; Van Ryan, 2010) and financial background which allows the entry only for huge corporations (Kaiser and Snyder, 2012; Ernst & Young, 2012; Osmundsen et al., 2010b).

The third category assumes locations with neither huge financial requirements nor heavy weather conditions, i.e. USA onshore as the Bakken Formation. In the following, this tight oil play will be analyzed with a special purpose of unfolding the factors that caused the given industry structure.

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financial determinants are very similar, factors influencing shale gas and tight oil are considered the same and in both cases relevant. Five main points have been listed, each with several additional features that can explain the tight oil boom and the high presence of small independents.

First of all, the investment structure of the tight oil industry requires different approaches.

Figure 2. – Cash flow of conventional and non-conventional projects

It comes from the fact that although the level of initial investment is low comparing to conventional production, the depletion rate of shale wells is extremely high (60 % in the first year) which requires constant drilling in order to maintain production (Luciani, 2013). So the cash flow of conventional oil and tight oil (shale gas) projects differ significantly (Figure 2.). It is clearly visible that comparing to the high initial cost at conventional wells, which is an insurmountable obstacle for a small company, shale wells have lower initial investment for more years with lower profitability and cash flow. Although it requires more operating expenditures due to the permanent drilling and fracturing processes, it is manageable from a small firm’s perspective (Herber, 2013). Furthermore these investments are risk, high-reward type investments that are too risky for the financial criteria system of the major companies. An additional fact is that small independents have no vast financial background, therefore the positive cash flow and growth is in the forefront, rather than being profitable in the long run (Maugeri, 2012).

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resources are owned by the landowner. Each landowner is basically incentivized for selling or leasing out the land that might contain hydrocarbons. This system greatly assisted the development of unconventional resources as the small independents had the opportunity to access the resources (Boersma and Johnson, 2013). Secondly it has to be mentioned that the used method of hydraulic fracturing is not prevalent and legally accepted all over the world, due to the environmental impacts. In Europe the method is heavily opposed, which inhibits the spread of extracting resources from shale plays (Economist, 2013). In the US no ban has occurred so far, although the opposition is intensified recently (Stevens, 2010).

The role of infrastructure is the third factor but remains an important factor for several reasons. First of all, the pipeline system is well-developed, although the transportation via pipeline has been hindered recently due to insufficient capacity (Carter, 2013). Secondly the ownership of transportation capacity rights is separated from the ownership of the pipeline itself, therefore market forces prevail, meaning that no barriers of entry exist and the access to market is smooth even for smaller firms (Medlock, 2012). Furthermore no long-term contracts bound the partners as in Europe, which allows quicker response to market conditions (Boersma and Johnson, 2013).

The fourth factor is the presence of service companies, which allowed the fast dispersion of the innovative technology. As extracting from shale plays requires new and efficient drilling technology i.e. multi-pad drilling, their knowledge was crucial when it comes to proficient and quick production (Stevens, 2010). They provided pioneering knowledge that helped small firms, which was at hand in other countries only at the end of the decade (IEA, 2013). In addition the drilling rig market which is highly flexible within the country, allows a relatively quick response to the market changes (Maugeri, 2012). One important fact has to be highlighted, that 55% of all the rotary drilling rigs in the world can be found in the United States (Williams, 2013).

The fifth factor is related to the domestic and broadening outlook of the oil and gas industry. As the US hydrocarbon production had reached its plateau in the middle of the 80s, since then a downward trend characterized the extraction, therefore the expectations were based on an import-dependent United States. As import of liquefied natural gas (LNG) seemed a proper solution for easing American hydrocarbon needs, the major and bigger oil companies were

investing massive amount in LNG terminals, thinking of it as the future of domestic supply

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Although various reasons could be adduced as to why small independents took the leading role, the constellation of the above mentioned factors contributed significantly to the current situation that evolved in the beginning of the century. These facts can explain why so many small firms could enter into the tight oil producing market, however, cannot explain why the production boom happened. In the next section, a correspondence among price and production is delineated based on the theory that tries to answer the cause of the production boom.

III. Hypotheses

The hypotheses are based on the argumentation of the technological view, with the aim of giving a practical understanding for the effect of prices through technology. It is described how it affects production and how companies are incentivized by higher levels of profit ratio. Although the reserves are an essential geophysical constraint on production, the decision itself on production is based on economics (Moroney and Berg, 1999). Following the previously described method, the presence of tight oil is known for several decades (Grape, 2006), but the above described extraction method was needed to make the extraction economically viable (Mohaghegh, 2013).

As this study approaches from the technological and economical side, the following two-stage hypotheses are related to the effect of prices on technology usage:

H1: Higher oil prices will increase oil production through technology as follows: H1a: Higher oil price positively affects the number of horizontal wells

H1b: Rising number of horizontal wells causes more production

In the Industry outlook section, it is described why the tight oil industry is rather characterized by small firms. As faster reaction and more efficient operations of small firms is assumed, the second hypothesis is devoted to examine how the rising oil prices affect the presence of companies according to their size.

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IV. Literature review

When it comes to scientific analysis of hydrocarbon production, several papers are available in the broad literature. Although the difference is not straightforward, these analyses can be formed into two main clusters; one that is close to the geographical view and one that is close to economic view. From the peak oil theory’s perspective, Hubbert (1956), who established the basics of the geological view created the basic model that focuses on the cumulative production. It focuses solely on geographical factors, excluding technological development, price change and assumes constant recovery rate. On the basis of cumulative production and proven reserves, this model predicted the American peak oil successfully. Relying upon this model, Campbell and Laherrére (1998) argue for the peak oil production of the global oil supply with the help of focusing on the new oil field discoveries. Although the economic factors are acknowledged, the emphasis is on the fact that the conventional fossil resources are finite. This study, however, forms the essentials for the proponents of the pure geological approach. Testing the Hubbert theory, Brandt (2007) used American and international oil producing regions in order to reveal the application of the once worked model and to try to predict the global peak oil. Among others it is concluded that the cumulative production is not a bell-shaped curve (that is symmetric), only in case of the US; meaning that the decline rates tend to be gentle that can postpone the date of peak production. Overall the weak applicability of the Hubbert model is concluded.

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are dealt with pinpointing that oil price is only one factor among many. Although the research addresses domestic energy security issues, the price as an incentive for production is accepted and acknowledged. Kaufmann and Cleveland (2001) use the same variables; however, the focus is on the criticism of the Hotelling and Hubbert model. Based on empirical evidence, the location selection of firms described in the Hotelling model is rejected. The paper rejects the Hubbert model claiming that the oil production of a region is not predetermined as the resource price is an important determinant factor as well. Furthermore it criticizes the extrapolation based on past and current production that is described in the Campbell and Laherrére (1998) model. The following two papers can be considered to be in the middle as both approaches are applied. Moroney and Berg (1999) used in the econometric model, the combination of physical reserve and economic variables as well, as it can give more precise future production projections. Benes et al (2012) adopt a mixture of the above mentioned methods and both the geographical and economic perspective is encompassed. This mix of econometric applications is predicting oil production and prices in a more appropriate manner. Cumulative production and lagged values of oil price are used with the strong assumption of the connection between higher prices and commercially viable technology usage. In accordance with the applied method, this analysis gives a solid basis for the thesis.

In line with the revised literature, it can be concluded, that the combination analysis of oil production is the closest to reality as the geological factor is one of the most dominant beside the economic factors. Although there are more economic determinants of oil production, in case of Bakken, the new technology is inevitable and extremely important.

V. Methodology

To estimate the model panel data regression is used, where cross-sectional (oil fields) and time-series (years) data is included as well. This balanced dataset covers 29 oil fields (i) and

13 years (t) from 2000 till 2012, therefore considered to be “short and wide” (Hill et al.,

2011). As a benefit of using panel data, starting with the fact that it controls for heterogeneity among oil fields as there are some differences in the characteristics of each field. In addition

multicollinearity problems can be eliminated and it gives more informative data (Baltagi,

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concerned, it can be difficult to design and collect the data as it has arisen with compiling the database about production numbers due to inaccessibility (Baltagi, 2005).

Panel data is not a solution for every bias; however, it is the best way to analyze the influence of changing oil prices on oil production through the decade and the tight oil fields in Montana State.

1. Data

To gain useful and important results one needs to access databases or datasets that are at disposal. In case of analyzing the industries closely related to oil and gas segment, the fact of data scarcity has to be taken into consideration (Osmundsen et al., 2010a). In the following section the collection and creation of the database is described and how the explanatory variable sets were composed.

a. Data source

Although the Bakken Formation stretches under two states in the USA, the production in Montana stands in the core of the analysis. As far as the oil production in Montana is concerned, the raw data is provided by the Department of Natural Resource and Conservation (DNRC) in Montana and its quasi-judicial body, the Montana Board of Oil & Gas Conservation (MBOGC) publications2.

Furthermore the publications and studies of Energy Information Administration (EIA) have been used, which institution deals with the shale oil phenomena prominently, providing useful, but rather technical information about the whole industry itself.

The third most important source is the United States Geological Survey (USGS), which examines the hydrocarbon resource-portfolio of the country, rather from a geological approach. The dataset stems largely from MBOGC, but as far as the geology is concerned, a large amount of information was drawn from EIA and USGS. All of them are considered official and reliable sources and handled as my primary sources.

A large part of the analysis concerning the sizes of corporations, data about the number of agents and number of employees was drawn from two main sources. Firstly financial reports and the U.S. Securities and Exchange Commission (SEC) filings of companies were used where available. Secondly company profile and information provider database (Manta,

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BusinessWeek, InsideView) was used, as in some cases no published company data could be found.

b. Database

In case of choosing panel data, one of the main practical disadvantages is that a vast amount of data is needed, which in this case was not at disposal. In order to surmount this problem, a database had to be created on the basis of the above mentioned raw information provided by the MBOGC. As far as the variables are concerned, annual data has been used, as in most cases only annual data was at disposal. As mentioned there are 29 oil fields as individuals and the timeframe of the analysis is 13 years. All the included fields are the lower, middle and the upper shale member of the Bakken Formation. In the study, 66 companies are included that were operating wells in the observed years. All the production values, number and types of oil wells are gathered separately for each field and each year, inserted into a matrix with all the variables. All the used variables are included in the database and described in the following section.

c. Variables

To start the analysis, the variables need to be specified as follows. On the left-hand side of the equation, the dependent variable describes the oil production. On the right-hand side of the formula the independent and indicator variables stand with the intention of capturing causes of production and changes in the outlook in the industry.

The dependent variable (PROD) is the tight oil production, expressed in barrels of oil (bbl) for each year. The value expresses the number of extracted barrel of oil per each year and per each oil field from the Bakken Formation in Montana.

The independent variables are as follows:

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WTI and Dubai), in this case the Montana crude oil first purchase price is used. The first purchase price is directly connected to the first arms-length transfer of ownership of crude oil at the first removal of the leased location. Using the purchase price makes it more precise and state-specific, but still represents the volatility of world oil prices as there is strong relationship among them. From a technological point of view, the price has a crucial role when it comes to introducing and using new technology. It is constant for each field, however differ over years. Current prices and additionally two lagged values of prices are used; with the intention of revealing the prompt and the longer-term effect on production.

The third independent variable (WELLS) shows the number of producing oil wells per each year per each oil field. The data is based on the well count of the DNRC. It is important as it can clearly impact the volume of production therefore excluding it can bias the results.

The percentage of completed horizontal wells (HORWELL) is the fourth and key variable regarding each year and field. The variable’s value shows the proportion of horizontally drilled wells in relation to the total number of wells. Using this variable as a proxy for technology, it can clarify the effect of the new technology on production and can test the hypothesis.

In the hypothesis beyond the effect of the oil prices on production, the oil price impact on the presence of companies of different sizes is scrutinized as well. The category variables are referring to categories of corporations on the basis of their size based on the number of employees. In order to examine how the rising prices affect the number of companies according to their sizes, two groups have been created as follows. In the first category (CAT1), firms with 0-500 employees, in the second (CAT2), the firms with over 501 employees have been put into. In the database, the number of employees represents the size of the staff at the time of entry in the given oil field. In addition with the help of the explanatory variables, the model is able to express how one unit increase in price affects the operating firms in the given category. That variable will help to reveal whether the second hypothesis stands its ground or not.

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when it comes to the production numbers. The third dichotomous variable (ELMCOULEE) is used to measure the importance of the Elm coulee oil field, which gives more than 90% of the production in the range of examined oil fields. The field has got a pivotal role; therefore measuring separately the impact of prices on production in the whole sample and only in this field would give more accurate results. The role of the fourth dummy variable (CRISIS) is to eliminate the potential bias stemming from the indirect effect of the financial crisis on oil production as the credit crunch could affect oil production through various other channels as well, i.e. constraint on financial support for small-scale independents. These last two dichotomous variables are not included into the model and are only for controlling the Elm coulee field bias and the effect of the crisis on price and production.

2. Model specification

As more hypotheses have been formed, more models need to be tested. Firstly, a basic model is described (Eq. 1.), with all the variables that can affect the volume as follows:

𝑙𝑛𝑃𝑅𝑂𝐷𝑖𝑡 = 𝛼 + 𝛽1 𝑙𝑛𝐶𝑈𝑀𝑃𝑅𝑂𝐷𝑖𝑡 + 𝛽2𝑃𝑅𝐼𝐶𝐸𝑡+ 𝛽3𝑊𝐸𝐿𝐿𝑆𝑖𝑡+ 𝛽4𝐻𝑂𝑅𝑊𝐸𝐿𝐿𝑆𝑖𝑡+

𝛽5𝐶𝐴𝑇1𝑖𝑡+ 𝛽6𝐶𝐴𝑇2𝑖𝑡+ 𝛿1𝐻𝑂𝑅𝐼𝑍𝑖𝑡+ 𝛿2𝑀𝐴𝐽𝑂𝑅𝑖𝑡+ 𝜀𝑖𝑡 (Eq. 1)

where the overall set of fields are present across the Bakken Formation. The model is estimated with current price and with lagged oil price as well (t-1 and t-2) respectively and

expected to capture the overall picture of production and its factors within the shale play. As the majority of the production comes from the accumulation named Elm Coulee, a re-estimation is provided in order to control for this field production. As the tight oil boom started at this oil formation, major differences could be occurred in the results. Additionally due to the effect of crisis on production, one year’s oil price (2009) is removed during the re-estimation in order to control for it.

In order to test the first hypothesis, a sub-model is created. According to the chosen and above described theory, the rising prices will make new technology economically feasible and accessible, therefore in Eq. 2, the effect of oil prices (re-estimated with current price and two years lagged values as well) on the usage of horizontally drilled wells are analyzed as follows:

𝐻𝑂𝑅𝑊𝐸𝐿𝐿𝑆𝑖𝑡 = 𝛼 + 𝛽1 𝑃𝑅𝐼𝐶𝐸𝑡+ 𝜀𝑖𝑡 (Eq. 2)

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𝑙𝑛𝑃𝑅𝑂𝐷𝑖𝑡 = 𝛼 + 𝛽1 𝐻𝑂𝑅𝑊𝐸𝐿𝐿𝑆𝑖𝑡+ 𝜀𝑖𝑡 (Eq. 3)

The Eq. 2 and Eq. 3 are devoted to show the indirect effect of the price alteration on production volumes.

As in the second hypothesis the reaction of the companies in both categories is analyzed, where each category reflects a specific size of companies. In the following a model is created regarding each company size. The first sub-model (Eq. 4) estimates the price effect on the small independents (under 500 employees), the second sub-model (Eq. 5) estimates the effect on medium-sized independents (up from 500 employees). With the purpose of receiving more precise information about the company’s reaction, a dummy variable is involved to check for the presence of major companies.

𝐶𝐴𝑇1𝑖𝑡 = 𝛼 + 𝛽1 𝑃𝑅𝐼𝐶𝐸𝑡+ 𝜀𝑖𝑡 (Eq. 4) 𝐶𝐴𝑇2𝑖𝑡 = 𝛼 + 𝛽1 𝑃𝑅𝐼𝐶𝐸𝑡+ 𝜀𝑖𝑡 (Eq. 5)

The expectation is to find evidence on the potential connection between oil prices and company entry. In case of all equations additional robustness checks have been applied in order to control for other indirect factors, and check how the variables are influenced within different conditions.

3. Estimation of the models

During the estimation methods, numerous factors can arise that can distort the results of the model, i.e. heteroskedasticity, heterogeneity and simultaneity bias as well. For each of the bias, several techniques are at disposal to correct for it (Greene, 2002). The presence of heteroskedasticity has to be observed, although it is more likely in cross-sectional or time-series regressions. To detect the presence, Legrange Multiplier / Breusch Pagan test needs to be performed. The null hypothesis is based on the variance function and expresses that if the variances of all observations are the same, the data is homoscedastic. As in this case the null hypothesis is failed to be rejected (χ2=3.22 and p= 0.0726), it is concluded that the data is homoskedastic (Hill et al., 2011).

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by using fixed effects special panel data model (described below). Regarding simultaneity, which is a reverse causality problem, in the case of the second explanatory variable (PRICE), it can be a potential threat; therefore it is dealt with its own lagged values. The model is re-estimated with current prices due to the possibility of releasing spare production capacity, and with one and two years lagged oil prices respectively (Kaufmann, 1991). Firstly, using lagged values is a tool to eliminate simultaneity bias. Secondly it may be possible to observe the number of years in oil prices that still affect production with setting up new production units. It has been presented before, that there are two main used techniques to analyze panel data: fixed effects and random effects (Baltagi, 2005). In the fixed effects model, it is assumed that different features in the oil fields can bias the explanatory variable therefore needed to control for it. In this case all slope coefficients assumed to be constant for all oil fields, as it stands for “short and wide” panel sets. All the characteristics among oil fields (individual heterogeneity) are assumed to be captured by the intercept; hence the heterogeneity problem is solved. These intercepts are called fixed effects. The created fixed effects eliminate the fields-specific, time-invariant characteristics from the explanatory variables and create an unbiased model, the fixed effects model. In the model a dummy variable is introduced (fixed effects estimator) for each oil fields with one exception in order to avoid perfect multicollinearity (Hill et al., 2011). In the random effects model, the individuals are randomly selected in the sample. Under the assumption of having no correlation between the error term and the explanatory variable, it is possible to include the time invariant variables as it is not absorbed by the intercept. Although omitted variable bias can occur, as individual characteristics need to be specified and it is not available in some cases (Greene, 2002). Here the individual differences are treated as random, while in fixed effects model, fixed.

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MAJOR). After the estimation of the augmented model with random effect, and testing the significance of the variables, the result (χ2=19.19 and p= 0.0018) shows that fixed effects model is the most suitable to use.

In accordance of the previously executed methods, the fixed effects model is used.

VI. Results

Although many factors can influence the oil production in general, the technological variables are emphasized due to the specificities of the Bakken Formation. Therefore this study covers only one part of the factors with an additional special focus on the industry outlook. In this section, the answers are divided into two parts; one is related to oil production and another to the presence of small enterprises.

1. Oil production

After running the basic model (Table 1) some conclusions can be drawn from the results. Using a combination model of geological and technological view turned out to be the right choice. Depletion seems to be prevailing, as the cumulative production has a highly significant effect on the production. This is supported by the current and lagged values of prices that have a negative effect on the production which is in line with the depletion and the real situation of the declining production among others due to geological facts (See Limitation) (Table 1). Although the connection between prices and production are negative, the indirect effect through technology seems to have a positive impact (Table 2 & 4. – Appendix). In the Methodology section the Eq. 2 and Eq. 3 describes the indirect effect of price change due to new technology. The first step (Eq. 2) assumes the positive effect of rising

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In the case of the second step, the presence of horizontally drilled well has a substantial effect on production as it was expected (Table 4. – Appendix). Moreover in the Table 1., the horizontal well dummy shows a significant effect on oil production. Focusing on the Elm Coulee field where it started by Lyco Energy, 1 % rise in the percentage of used horizontal wells comparing to all the oil wells affects the production by 12.6 %, with high value of R2 (0.84).

Table 1. - The basic model from Eq. 1

(a) (b) (c) (d)

VARIABLES lnprod lnprod lnprod lnprod lncumprod 0.386*** 0.396*** 0.365*** 0.434*** (0.0422) (0.0459) (0.0487) (0.0440) price -0.0254*** -0.0277*** (0.00356) (0.00352) wells -0.00630** -0.00626** -0.00560* -0.00618** (0.00265) (0.00273) (0.00292) (0.00264) horwells 0.0183*** 0.0202*** 0.0210*** 0.0204*** (0.00451) (0.00465) (0.00488) (0.00461) cat1 0.776*** 0.841*** 1.195*** 0.723*** (0.124) (0.142) (0.194) (0.123) cat2 0.524 0.555 0.670* 0.628* (0.359) (0.364) (0.374) (0.366) horiz 2.925*** 2.590*** 2.434*** 2.465*** (0.485) (0.496) (0.510) (0.493) major -1.018 -1.688* -4.669*** -1.200 (0.819) (0.956) (1.462) (0.805) L.price -0.0287*** (0.00383) L2.price -0.0286*** (0.00441) Constant 2.121*** 2.038*** 1.779*** 1.986*** (0.303) (0.332) (0.380) (0.305) Observations 377 348 319 348 R-squared 0.679 0.683 0.685 0.708 Number of field2 29 29 29 29

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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After using the combination of geological and economic approaches, it can be concluded that the geological factors are prevailing and fundamentally impact the amount of extractable resources. The technological line of reasoning, however, is confirmed considering the connection between the prices, new technology and increasing production. Based on the results, the first hypothesis (H1a & H1b) is approved.

2. Presence of small independents

One of the cornerstones of this analysis is the behavior of firms. According to the results, small independents have a more significant and important role in production (Table 1.) than middle-sized or major firms when we observe the results along the lagged values of prices. It seems to be confirmed, that the small independents with employees not more than 500, had a key role in the tight oil production in the past decade in Montana. And what is more important, this role is not that significant in the case of middle-sized companies and majors.

Table 6. - The mixed model from Eq. 4 and Eq. 5

(a) (b) (c) (d) (e) (f)

VARIABLES cat1 cat2 cat1 cat2 cat1 cat2 price 0.00737*** 0.00366*** (0.00175) (0.000829) major 3.546*** 2.260*** 4.444*** 2.523*** 5.987*** 2.986*** (0.336) (0.159) (0.309) (0.167) (0.254) (0.174) L.price 0.00477*** 0.00337*** (0.00159) (0.000859) L2.price 0.000437 0.00285*** (0.00134) (0.000915) Constant 0.585*** -0.136*** 0.748*** -0.111** 0.967*** -0.0768 (0.103) (0.0487) (0.0892) (0.0482) (0.0691) (0.0473) Observations 377 377 348 348 319 319 R-squared 0.283 0.401 0.416 0.448 0.664 0.533 Number of field2 29 29 29 29 29 29

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Comparing the price effect on smaller and medium-sized independents, it is clearly shown in the Table 6., that price has higher effect on the numbers of small independents than on medium-sized firms (column a, b, c, d). The rising trend of the value of the R2 in both cases indicates that the number of firms is affected stronger by the prices in the previous year, namely the lagged values (column c, d, e, f), however, with smaller impacts. If only the Elm Coulee field is examined, the trend shows similar but stronger price impact on firms, probably due to the intense presence of both categories (Table 6a. & 6b. – Appendix, column d, e, f). Moreover there is a slight, but important difference between the two categories, notably the time horizon of the price effect. It seems to support the theory, that smaller firms focus on shorter pay-off and take shorter period of time into consideration as the second lag is significant only in case of the Elm Coulee field (Table 6a., column f – Appendix), but not in the basic model (column e) and in the model where the crisis in controlled for (Table 6a., column i). Furthermore the effect of oil price on independents in the Elm Coulee field (column d, e, f) has to be highlighted. With high values of the R2, it seems that it can be concluded, that among others the price is indeed a relatively important determinant of entry, which therefore impacted considerably the tight oil production.

On the basis of the analysis, a far-reaching indirect line of reasoning appears to be outlined. From 2000 till 2012 the oil price had a significant and important influence on the presence of small independents (Table 6.). The smaller independents had a significant impact on the share of horizontal wells per total numbers of oil wells (Table 3. - Appendix). The horizontal wells clearly had a positive strong effect on the number of extracted barrels (Table 4. - Appendix). The described statement could show a possible strong, but indirect link between oil prices and tight oil production.

As far as the second hypothesis (H2) is concerned, the rising oil prices have a significant positive effect on the presence of independent companies, and what is more important is, this impact is significantly greater than on medium-sized firms. Based on the results, the hypothesis is approved.

3. Limitation

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accumulation can be found. Therefore the analysis is not representing the whole Bakken Formation. Furthermore among the 29 oil fields the majority was producing for decades, a clear declining curve could be observed. Due to the small sample and the fact, that it cannot be representative for the whole Bakken production, the coefficients are not surprisingly negative between the prices and production. Nonetheless it can confirm the strong effect of depletion, in addition it has to be mentioned, that not only depletion can play a role, but the suction power of the production boom in North Dakota, which is indeed an existing phenomena.

When it comes to the small firms, another restriction which requires more detailed analysis is the static capture of firms about the entry into an oil field. As the size of companies is captured only at the time of entry, the model cannot handle the expansion of firms during the examined timeframe.

The tiny values of the regression table are expected (Table 6), as the number of operating companies and their changes in most of the fields were really low within the decade. Meaning that more wells were operating by few operators, and even a small change (quit or entry of one firm) could change the outlook substantially. In the Elm Coulee field, more players were present and greater change could be observed, therefore the values are bigger.

VII. Conclusion

The initial purpose of the thesis was to shed light on tight oil production and the high presence of small independent companies.

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IX. Appendix – Figure & Tables

Figure 1. – Oil production in the selected US tight oil plays

Table 2. – The effect of oil price on the share of horizontally drilled oil wells

(1) (2) (3) VARIABLES horwells horwells horwells price 0.327*** (0.0481) L.price 0.310*** (0.0531) L2.price 0.337*** (0.0622) Constant -5.471* -3.050 -2.345 (2.825) (2.988) (3.256) Observations 377 348 319 R-squared 0.118 0.097 0.092 Number of field2 29 29 29

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Table 3. - The effect of oil price on the share of horizontally drilled oil wells – robustness check

(1) (2) (3) VARIABLES horwells horwells horwells price 0.273*** (0.0487) cat1 4.014** 7.045*** 13.90*** (1.694) (1.998) (2.649) cat2 4.616 5.296 7.881** (3.574) (3.698) (3.870) major -2.552 -21.44 -82.13*** (11.50) (14.01) (21.22) L.price 0.247*** (0.0527) L2.price 0.288*** (0.0602) Constant -7.679** -8.073** -15.14*** (3.060) (3.348) (4.034) Observations 377 348 319 R-squared 0.167 0.171 0.206 Number of field2 29 29 29

Table 4. – The effect of horizontal oil well on oil production

(1) (2) (3) VARIABLES lnprod lnprod lnprod horwells 0.0602*** 0.126*** 0.0476*** (0.00459) (0.0113) (0.00471) Constant 4.570*** 1.781* 4.538*** (0.117) (0.878) (0.107) Observations 377 26 351 R-squared 0.331 0.843 0.240 Number of field2 29 2 27

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Table 5. - The effect of horizontal oil well on oil production – robustness check

(1) (2) (3) VARIABLES lnprod lnprod lnprod horwells 0.0484*** 0.0484*** 0.0484*** (0.00425) (0.00428) (0.00429) cat1 1.031*** 1.044*** 1.047*** (0.106) (0.140) (0.141) cat2 -0.0393 -0.0257 (0.267) (0.296) major -0.103 (0.950) Constant 3.546*** 3.536*** 3.536*** (0.148) (0.162) (0.163) Observations 377 377 377 R-squared 0.475 0.475 0.475 Number of field2 29 29 29

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Table 6a. - The effect of oil price on the number of firms in category 1

(a) (b) (c) (d) (e) (f) (g) (h) (i)

VARIABLES cat1 cat1 cat1 cat1 cat1 cat1 cat1 cat1 cat1

price 0.00737*** 0.0722*** 0.00737*** (0.00175) (0.0202) (0.00175) major 3.546*** 4.444*** 5.987*** 3.081** 4.036*** 4.886*** 3.597*** 4.500*** 6.158*** (0.336) (0.309) (0.254) (1.214) (0.889) (0.809) (0.339) (0.313) (0.247) L.price 0.00477*** 0.0599*** 0.00554*** (0.00159) (0.0157) (0.00184) L2.price 0.000437 0.0373** 0.000117 (0.00134) (0.0162) (0.00132) Constant 0.585*** 0.748*** 0.967*** 0.377 1.027 2.180*** 0.571*** 0.717*** 0.971*** (0.103) (0.0892) (0.0691) (0.979) (0.759) (0.679) (0.104) (0.0955) (0.0663) Observations 377 348 319 26 24 22 348 319 290 R-squared 0.283 0.416 0.664 0.664 0.783 0.835 0.302 0.444 0.712 Number of field2 29 29 29 2 2 2 29 29 29

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Table 6b. - The effect of oil price on the number of firms in category 2

(a) (b) (c) (d) (e) (f) (g) (h) (i)

VARIABLES cat2 cat2 cat2 cat2 cat2 cat2 cat2 cat2 cat2

price 0.00366*** 0.0461*** 0.00369*** (0.000829) (0.0101) (0.000833) major 2.260*** 2.523*** 2.986*** 1.295** 1.769*** 1.953*** 2.201*** 2.434*** 2.908*** (0.159) (0.167) (0.174) (0.605) (0.604) (0.671) (0.161) (0.171) (0.182) L.price 0.00337*** 0.0406*** 0.00408*** (0.000859) (0.0106) (0.00101) L2.price 0.00285*** 0.0375** 0.00294*** (0.000915) (0.0134) (0.000972) Constant -0.136*** -0.111** -0.0768 -1.259** -1.006* -0.681 -0.142*** -0.135** -0.0776 (0.0487) (0.0482) (0.0473) (0.488) (0.516) (0.564) (0.0494) (0.0524) (0.0488) Observations 377 348 319 26 24 22 348 319 290 R-squared 0.401 0.448 0.533 0.710 0.702 0.691 0.405 0.450 0.528 Number of field2 29 29 29 2 2 2 29 29 29

Tight oil production and the industry outlook in the Bakken Formation

M.Sc. International Economics and Business - University of Groningen

M.A. International Economy and Business - Corvinus University of Budapest

Double Degree Program

Tight oil production and the industry

outlook in the Bakken Formation

Adam Holczer

Abstract

Table of contents

I.

Introduction

II. Theory

1. Industry outlook

III. Hypotheses

IV. Literature review

V. Methodology

1. Data

a. Data source

b. Database

c. Variables

2. Model specification

3. Estimation of the models

VI. Results

1. Oil production

2. Presence of small independents

3. Limitation

VII. Conclusion

VIII. References

IX. Appendix – Figure & Tables