techno-economic modelling of tight oil production1090802/... · 2017. 4. 25. · modelling of tight...

IT 16 093

Examensarbete 15 hpJanuari 2017

Techno-Economic Modelling of Tight Oil Production A Bottom-up Approach

Per Hedbrant

Institutionen för informationsteknologiDepartment of Information Technology

Teknisk- naturvetenskaplig fakultet UTH-enheten Besöksadress: Ångströmlaboratoriet Lägerhyddsvägen 1 Hus 4, Plan 0 Postadress: Box 536 751 21 Uppsala Telefon: 018 – 471 30 03 Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student

Abstract

Techno-Economic Modelling of Tight Oil Production:A Bottom-up Approach

Per Hedbrant

There has been a revolution in US oil production the last decade, mainly because of production of the unconventional tight oil, and it is therefore of great interest to be able to produce reliable forecasts on future supply.

The aim of this study is to develop and explore a bottom-up well-by-well model for tight oil production. The model is based on the inherent physics and geology of the well, together with simple micro-economic principles. The model is made to be modular, flexible and well grounded in practicalities. It successfully manages to replicate historical production profile of Eagle Ford Play both with and without economic parameters. This implies the suitability of a bottom-up approach for this kind of task.

The model also tries to look into the future. An exploratory simulation result suggests that a large decrease and stagnation in drilling capacity gives a convergence in oil production to a constant level. But, the decrease in drilling capacity does not correspond with the decrease in oil production. Also, a low level of future oil price could give a hyperbolic decline in production rate which does not seem to level off within years.

Tryckt av: Reprocentralen ITCIT 16 093Examinator: Jarmo RantakokkoÄmnesgranskare: Mikael HöökHandledare: Henrik Wachtmeister

Contents

Prewords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

1 Introduction 1

1.1 Tight Oil and Oil Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Modeling of Oil production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Aim of Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3.1 Input and Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Method 4

2.1 Introduction to Modeling Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1.1 Production curve of a Single well . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1.2 Hyperbolic Decline Curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1.3 Curve of aggregated production . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.4 Net present value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Model Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.1 The well . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.2 The Producer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.3 The Rig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.4 Producer strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2.5 Net Present Value Estimation and Net Revenue Calculation . . . . . . . . . . . . 8

2.2.6 Additional Strategy Dimension: Aggressive vs Conservative Producer . . . . . . 9

2.2.7 Data Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

i

3 Results 11

3.1 Model evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.1 Evaluation using Historical Data on Rigs . . . . . . . . . . . . . . . . . . . . . 11

3.1.2 Evaluation with Economic Dynamics . . . . . . . . . . . . . . . . . . . . . . . 12

3.2 Explorative scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.2.1 Future Drilling Capacity Kept Constant . . . . . . . . . . . . . . . . . . . . . . 13

3.2.2 Future Oil Price Kept Constant . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 Discussion 16

4.1 Limitations of the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.2 Future Research and Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5 Conclusion 19

Bibliography 20

Appendices 24

A Decline Curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

B The Effect of wells Per Rig rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

C Histogram of Decline Curve Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 25

ii

Preface

To all hardworking baristas and waiters who have served me extensive amounts of cafe con leche on thecampus of Universidad Politecnica de Valencia, Spain, in October 2015.

iii

1 Introduction

1.1 Tight Oil and Oil Production

Tight oil and tight gas are extracted from shale formations or tight sandstone.1 It has only been in largescale production for about 15 years and was until recently limited to North America. Today, tight gasproduction stands for half of the total gas production and half of the total oil prodution in the U.S. (EIA;2015c). Technical advancement finally made the resources in the shale formations possible to extract andhigh global oil prices made it commercially potent. Horizontal drilling and advanced hydraulic fracturing(fracking) in the shale formations has revolutionized the U.S. production of crude oil, something that wasnot possible or affordable before the last millennium.

Since the mid 1980’s, there has been a steady decline in U.S. oil production but after the millenniumit has been replaced with a steady incline, which solely can be attributed to tight oil, see figure 1.1.If this dramatic change in domestic supply continue, it will decrease the United State’s dependence offoreign oil which will change the global oil flow and impact producers around the world (Grushevenkoand Grushevenko; 2012).

Figure 1.1: Monthly production of crude oil in the U.S. from January 1920 until July 2015. The produc-tion reached a maximum in October 1970 and showed a steady decline after the mid 80’s. However, thishas been replaced with a steady incline since the year 2008. [Source: EIA (2015c)]

1Tight oil and tight gas are also known as shale oil and shale gas.

1

MODELLING OF TIGHT OIL PRODUCTION

CHAPTER 1. INTRODUCTION

1.2. MODELING OF OIL PRODUCTION

This revolution of production is much desired elsewhere. The most promising regions for recoverableresources outside of North America are located in Russia, China, Argentina and Libya (EIA; 2015d).These countries are putting an increased hope in tight oil, together with other unconventional oil types,hoping that the oil production will be kept high even when the conventional oil will decrease in produc-tion. However, there is some debate about whether the first estimation of the tight oil resources are overlypositive (Hughes; 2015; Inman; 2014).

In any case, it is greatly important to be able to produce reliable outlooks on future supply of oil. Though,this has shown not to be easy, and both technical, geological and economical variables are involved andbehave inter-dependently of each other. As Jakobsson et al. (2014) described the situation: ”Forecastsand scenarios of future oil production present an embarrassment of riches.” (Jakobsson et al.; 2014,p. 113) The debate is polarized (Bentley and Boyle; 2008; Campbell and Wostmann; 2013; Campbell;2013). The term ’peak oil’ has been used to describe a global maximum rate in oil extraction, and someresearchers believe we will see a global ’peak oil’ production in the near future (Aleklett and Campbell;2003; Bentley; 2002), or that we have already past it (Aleklett et al.; 2010). Other researchers mean thatwe will see sufficient supply as long as we have a demand (Tilton; 1996; Gregory and Rogner; 1998;Odell; 2004; Mills; 2008).

Nonetheless, the considerable amount of research during the last decades have generated some degreeof consensus concerning the driving mechanisms and fundamental limitations of the production. A peakin production is likely to be seen within coming decades, despite significant occurrences of unconven-tional oil resources . This probable maximum is due to increasing rate of decline in existing productionof conventional oil and decreasing rate of additions of new resources. The UK Energy Research Centre(UKERC) has undertaken extensive research on the topic and suggests that the production rate of con-ventional oil will likely peak before 2030, with a significant risk that it will occur before 2020 (Sorrell etal.; 2010b,a).

With similar stand, IEA (2010) predicted the conventional production to be kept on a plateau level, neverreaching its maximum in 2006. This has shown to be accurate according to data from Rystad Energy AS(2016b).

1.2 Modeling of Oil production

The divergence of different outlooks could be a result of different modeling approaches and their under-lying assumptions (Jakobsson et al.; 2014). Jakobsson et al. suggests two different classes of modeling,namely Top-down and Bottom-up. Top-down extrapolates from aggregate variables. Examples are sim-ple curve-fittings, system dynamic simulations and macro-econometric models. Bottom-up models try toreplicate the supply chain of the upstream oil industry and the forecast is typically the sum of productionfrom smaller units.

One of the advantages of top-down models is the simplicity. With a simple curve-fitting, a strong conclu-sion could be made. A well-known example is the projected curve of U.S. oil production that M. KingHubbert presented in 1956. Hubbert described a mathematical model which was based on strong andsimplifying assumptions, and he was the first one to clarify that. Even still, this simple model is indepen-dent of the geography and could foresee not only a maximum in oil production in the U.S. but but haslater also been applied to Norwegian oil peak production(Anderson and Conder; 2011).

Three advantages of bottom-up models are modularity, flexibility and concreteness. Modularity refers tothe degree to which a model is able to isolate single components, and where each component is based

Per Hedbrant 2 January 15, 2017


CHAPTER 1. INTRODUCTION

1.3. AIM OF STUDY

on clear assumptions. It is arguably easier to come to scientific consensus about smaller units rather thana full-scale model. It is also easier to identify weaker links in the model. Flexibility refers to the abilityto accommodate both economical, physical and technological principles. Concreteness refers to the factthat it is easy to see the links between modeling concepts and observable objects (Jakobsson et al.; 2014).

On top of these advantages, bottom-up models are widely used in the industry (Jakobsson et al.; 2014).International Energy Agency and the U.S. Energy Information Administration both use them for theirreports World Energy Outlook and Annual Energy Outlook respectively.

This study tries to model the drilling of several thousands of wells in a relatively large region, a so calledgeological play. Every individual well has a unique initial production and following decline rate. Tomodel production of a whole region a bottom up approach is therefore necessary. In such an approachproduction and profitably are estimated for each well and then summed up for regional aggregates.

1.3 Aim of Study

The aim of the study is to develop and explore a regional bottom-up well-by-well model for tight oilproduction. The model is based on the physical and geological nature of the well, simple micro-economicprinciples concerning the producer, and is evaluated by comparison to actual producition data for theEagle Ford play. It is then used to derive explorative scenarios of future production for this region.

The model is implemented in MATLAB and sumulates the producer’s decision to invest or not in newproduction according to estimated net present value of new wells. Key Model parameters are oil wellproduction, oil well costs, available drilling rigs and the wells per rig rate, available financial capital ofproducer and oil price.

1.3.1 Input and Validation

The model is designed in order to handle different kinds of input. Each model version has the followinginputs:

1. Historical data of drilling rigs and oil price, in order to recreate regional aggregate production.

2. Linear and dynamic input profiles of drilling rigs and oil price, for tractable conceptual experi-ments.

3. Random sampling input of drilling rigs and oil price, for realism and sensitivity/uncertainty anal-ysis.

The model is validated by achieving the following:

1. Recreating Eagle Ford production profile from drilling rig data (EIA; 2015b).

2. Recreating Eagle Ford profile from upper constraint on drilling capacity and oil price.


2 Method

In section 2.1, theory of the the modeling methodology is presented. In section 2.2 the actual model ispresented.

2.1 Introduction to Modeling Methodology

2.1.1 Production curve of a Single well

The history of analyzing the production curve of conventional oil and gas is probably as old as the historyof actual production. Interesting facts are how much oil a well can produce accumulated over the time- called (ultimately) recoverable resources (EIA; 2016c). It is also important to know when this oil isrecovered and supplied to the market.

Arps (1945) laid the foundation of today’s decline curve analysis when he proposed the exponential andhyperbolic decline curves. He derived different types of equations of production rate from productiondata. Later researchers came to realize that one of these is the same as the physical flow of a homogenousfield with a given initial drive pressure that is reduced by extraction (Hook et al.; 2013). As the theexponential and hyperbolic decline curves are justified by physiological and geological theories, theyare more preferred comparred to other a posteriori justified models like for example the the Power-LawExponential Model (Ilk et al.; 2008), the Stretched Exponential Model (Valko; 2009), and the LogisticGrowth Model (Clark et al.; 2011).

Lund et al. (2015) agreed on this, and did a decline curve analysis by examining 2 311 wells from theEagle Ford Tight Oil play. They concluded that only 11% on average of the first year’s total productioncomes from before the peak production. In most cases, the maximum production rate is reached in thefirst or second month and the production before the peak could therefore be neglected. Therefore, themonotonically decreasing hyperbolic decline curve could be used.

2.1.2 Hyperbolic Decline Curve

The hyperbolic decline curve is given by

q(t) =q0

[1+Db(t − t0)]1/b (2.1)

4


CHAPTER 2. METHOD

2.1. INTRODUCTION TO MODELING METHODOLOGY

where q(t) is the production rate at time t, usually expressed in barrels per day or barrels per month. q0is called the initial production rate, but practically it is the peak production rate which is usually the firstmonth of production. q0 is expressed in the same unit as q(t) is. D and b are decline curve parameterswith physical meanings.

Figure 2.1 is a plot of the curve using the parameter values b = 1.10 and D = 0.488. These values werefound to be the mean values for wells in the Eagle Ford shale play, found by Lund (2014). See figures inAppendix A how the values of parameters b respectively D affect the decline curve.

Figure 2.1: A sketch of a theoretical production curve of a single well, showing the percentage of pro-duction comparred to the first production month. Four points are marked on the curve: month 4, month5, month 60 and month 120.

When using the hyperbolic decline curve approximation, the production reaches its maximum during thefirst month of production. During the second month the theoretic production is dramatically decreased,and during the third month it is only around half of the initial production rate. This means that a substan-tial amount of oil is produced during the first few months. Although the decline tapers after a couple ofyears, which means that a well could be kept in production during several years.

2.1.3 Curve of aggregated production

Figure 2.2 shows the aggregated production of 50 wells, one well drilled each month until month 50. Assoon as no new wells are drilled the total production reaches a maximum and dramatically decreases.The production losses half of its rate in 1-2 years.



CHAPTER 2. METHOD

2.2. MODEL DESCRIPTION

Figure 2.2: Aggregated production rate of 50 wells.

2.1.4 Net present value

Net Present Value (NPV) is a well-known metric for evaluating an investment Ross (1995), given byequation 2.2. Net Present Value is the difference between the present value of the cash inflows and thepresent value of cash outflows. A positive value means that the anticipated revenue of the investment islarger than its anticipated costs, whilst a negative value indicates the opposite. The formula takes intoaccount the timings of the cash flows as each cash in- and outflow is discounted according to when itappears;

Net Present Value =T

∑t=0

Ct

(1+ r)t . (2.2)

In the equation, T is the time horizon, Ct is the cash flow at time t, and r is the discount rate. Thediscount rate is usually the interest rate from the best alternative investment but should also reflect therisk or uncertainty of future cash flows.

2.2 Model Description

The bottom-up Matlab model tries to replicate the oil production from single wells operated by oil pro-ducers. Each well and each producer is given individual attribute values. The time horizon of the modelis set by the user, for example 100 months. The time step of the model is one month, this because it ispreferable be able to compare with data given monthly by (EIA; 2015b) in their the Drilling ProductivityReport.

The most important outcome is the total production rate. Thsis is made up by the production of all thewells, and each well is an own unit, which could be given different attribute values. A well is drilled by aproducer, the producer could have different strategies, and different constraints and conditions could beput on different producers.



CHAPTER 2. METHOD


2.2.1 The well

A single well has the following key attributes;

• Production rate profile q(t).

• Initial production rate, q0.

• Decline parameters b and D.

• The month when it is set in production.

• The month when it is decommissioned.

The production profile is produced during the simulation by using the decline curve eqation (see eq. 2.1).Thus, when a well is first created, it is given the initial production rate, the decline curve parametersb and D, the starting month of production given by the sum of the producer’s attributes drill time andcompletion time into the future. At each time step the net profit (or net loss) from the well is added (ordeduced) from a producers capital, and the various costs are used.

2.2.2 The Producer

A producer have the following attributes:

• A portfolio of all its wells.

• An array recording the number of wells drilled each month.

• The producer’s strategy of when to invest in a new well.

• The number of months it takes for this producer to drill a well.

• The number of months it takes for this producer to complete a well after it has been drilled.

• The number of rigs the producer has available each month, or the number of wells the producerinvest in each month.

• An time-depending parameter for giving the initial production of new wells time-dependence.

2.2.3 The Rig

A well is drilled by a rig. In this model, rigs themselves are not distinguishable object with individualattributes. They are merely represented as a maximum number available to the producer each month.For simplification, all rigs is assumed to have a constant capacity of drilling one well per month. Thisassumtion goes in line with statistics from EIA. In their Monthly Productivity Report they conclude:”EIA has observed that the best predictor of the number of new wells beginning production in a givenmonth is the count of rigs in operation two months earlier.” (EIA; 2015b, p. 10).



CHAPTER 2. METHOD


2.2.4 Producer strategies

A producer drills wells according its given strategy, or according to a set number each month. The modelcan handle multiple producers which means that the user can compare producer with different strategiesand different constraints and opportunities. When the producer is given a set number each month, eco-nomical parameters as for example the oil price is disregarded. If not, these are the economical strategies:

1. ‘rig’: The rig Strategy. The producer disregards the economics and drills a well if a rig is available.This means that the input for available rigs times the wells per rig rate equals the number of wellsinvested at a particular month, which is called the drilling capacity.

2. ‘NPV rig’: The Net Present value + rig strategy. The producer estimate the net present value ofthe investment by assuming a constant oil Price and by using costs that were given the producerbefore the start of the simulation. The input of rigs times the wells per rig rate equals the maximumnumber of wells to be invested in each month. If the NPV is positive, this number number of rigsare all invested in, if the NPV is negative no investment is done. In this sense, the NPV estimatoris implemented as a binary regulator.

3. ‘cash’: The cash strategy. A well is drilled if the producer has capital to pay for the capital costs ofthe investment, namely the drilling cost and the completion cost.

4. Any combination of the above. For example the combination of all three of them. Then, a well isdrilled if the estimated NPV is positive, the producer has available capital for the capital costs anda rig is available.

2.2.5 Net Present Value Estimation and Net Revenue Calculation

If an economic strategy is persued, the decision whether to drill a well or not is based on the estimationof the investment’s Net Present Value. In order to do the estimation of the NPV, some assumptions andsimplifications are needed. The future oil price needs to be estimated. In the model, this needed to bedone in a simple and automized. This could have been done in several ways, for example by using themean of the last data points, or by using some sort of curve fitting technique to extrapolate. In this modelhowhever, the oil price was estimated merely and simply on the last available price. Furthermore, theproducer is assumed to be able to sell the oil immediately after producing. In reality, this is not the casefor all producers because of varying access to transport infrastructure such as oil pipeline and raleways.See for example Curtis et al. (2014) for further discussion.

The following describes both the estimation procedure and the calculation of the actual revenues andcosts. The monthly net cash flow is calculated by subtracting the monthly costs from the monthly rev-enue and discount the net value accoring to the timing. The different costs are devided into differentcategories. The royalties and taxes are not separated and are represented by a percentage of the revenue,so does the variable operating cost. The fixed operating cost is the same amount each producing month.Additionally, a drilling cost is incurred the same month as the investment decision and a completioncost is incurred each consecutive months until the well is put into production, typically 24 to 27 daysaccording to historcal data(Rystad Energy AS; 2016a). The 24 to 27 days could be rounded up to a fullmonth due to site preparation. Therefore, the default settings in this model is one month of drilling andone month of completion before the well is producing.

According to Rystad Energy AS (2015), the break-even oil prices in the Eagle Ford play has been around70 USD/bbl in years 2012 and 2013, and then gone down to around 65 USD/bbl in year 2014 and further



CHAPTER 2. METHOD


down to around 50 USD/bbl in 2015. These approximate break-even points have been used to adjust thevarious costs, though the time varying nature of the break-even apporixamates were not facilitated in themodel, i.e. the costs in the model are not implemented as time varying but as constants.

2.2.6 Additional Strategy Dimension: Aggressive vs Conservative Producer

As an additional dimension to the NPV strategy, instead of comparring the NPV to zero, the producercould compare it to another amount. Then, a producer with an aggressive investment strategy will drilla well if the NPV is larger than a given negative number while a conservative producer only drills if theNPV is larger than a given possitive number.

2.2.7 Data Input

From excel files, the model can load monthly data on oil price, the wells per rig rate and available rigs. Allthese pieces of data represent a time-varying variables and are therefore given on the form of a column orrow ranges in Excel-files. The oil price is the same for all the producers and is simulated by an averageprice per month. The default data is the monthly average oil price on West Texas Intermediate, taken fromEIA (2015a). Among other benchmark crude oil - including Brent Blend, Dubai and Oman - the WestTexas Intermediate is one of the most commonly used in North America to serve as a price reference(EIA; 2016b).

The data on available rigs is in the model specified for each producer. For the model evaluation, figuresof the collectively employed rigs each month at Eagle Ford Play has been retrieved from EIA (2015b).According to the monthly drilling activity by EIA (2015b), a rig has gone from drilling 0.8 wells permonth to drill around 2 wells per month. To accomodate this variance in the model, a ‘wells per rig rate’has been implemented. This variable could either be set as a constant value over time or be loaded froman excel file. The default values however, are based on data from EIA (2015b) for the year 2010, and onRystad (2015) for the years 2011-2015. Other data imports could be historical production. EIA (2015b)publishes monthly data on the production over the Eagle Ford tight oil play, among other regions.

Statistical data on decline parameters b and D together with initial production rate q0 are taken fromLund et al. (2015) in their not yet published paper. The initial production rates are from 2 311 wellsin the Eagle Ford Tight Oil play, and the wells were drilled during the years 2010-2014. The data canbe seen as histogram, the one for q0 in figure 2.3 and for b and D in appendix C. Moreover, the initialproduction rate is presented by the cummulative distribution function in 2.4 and the summarizing table2.1.

Table 2.1: Summary of statistical data on initial production (q0) from Lund et al. (2015).

2010 2011 2012 2013 2014Number of wells 64 286 556 926 479mean 380 492 513 512 539min 23 0.1 2 0.2 0.3max 1816 1691 2861 2440 3644



CHAPTER 2. METHOD


Figure 2.3: Histograms of the initial production rate, q0, for the years 2010-2014.

Figure 2.4: Cummulative distribution function of q0, for the years 2010-2014.


3 Results

3.1 Model evaluation

3.1.1 Evaluation using Historical Data on Rigs

The model is evaluated when using only available drilling rigs as a determinant of how many wells tobe invested in. In this mode, the producer strategy is set to ‘rigs’ and the model uses historical numberof employed rigs in the Eagle Ford play (EIA; 2015b). The initial production rate q0 and the declinecurve parameters b and D are set differently for the two simulations. For the blue curve, these are setas deterministic constants derived as means, b = 1.0 and D = 0.35, and q0 = 380,492,513,512 or 539bbl/day depending on the year the well is invested in, the first value for year 2010, second value for 2011et cetera. For the green curve these values are set stochastically using distribution functions based on thedata from Lund et al. (2015).

Figure 3.1 shows the two simulation results in coloured curves together with historical production rateof Eagle Ford play for the period January 2010 to December 2015 (EIA; 2015b). The two simulationsreplicate the historical data with an mean-squared-error 266 for the simulation with deterministic param-eters and 311 for the simulation with stochastic parameters. The simulated production rates are higherin almost all the time steps, except in the first few steps. The low production rate in the beginning isbecause the simulation starts with no active wells and with no production whilst the historical productionrate starts at a low level. The difference between the two simulations is only significant after the secondhalf of year 2014, but increases during the rest of the simulation.

11


CHAPTER 3. RESULTS

3.1. MODEL EVALUATION

Figure 3.1: Simulated and historical production rate for Eagle Ford play over the period January 2010 toDecember 2015. The blue curve represents a simulation using deterministic constants as initial produc-tion rate q0 and curve parameters b and D. The green curve represents a simulation using stochastic q0,band D. Data on historical production rate is taken from EIA (2015b)

3.1.2 Evaluation with Economic Dynamics

When using micro-economic dynamics in the model, the producer strategy is set to ‘NPV rig’ and thedrilling capacity and an estimation of the net present value decide how many wells are drilled eachmonth. The costs of the wells are adjusted in order to replicate the break-even points that Rystad EnergyAS (2015) presented. The simulation of three different cost settings together with two different settingsfor q0,b and D gave the production profiles presented in figure 3.2. The three curves with circles rep-resent simulations using stochastically given q0,b and D. The three other curves with squares representsimulations using deterministically given q0,b and D, the values used were the same as in section 3.1.1.

The result of these simulations are almost identical as the corresponding simulations in section 3.1.1except for timesteps of early 2015 and onwards, see figure 3.2. Two of the simulations – those withbreak-even oil price of 50 USD/bbl – are significantly closer to the historial production profile comparedto the other simulations. Before December 2014, the NPV estimation was not constraining the produc-tion, i.e. it was always positive and the all the rigs were employed to drill new wells. But after this tippingpoint the NPV calculations resulted in only negative values for the four simulations with break-even oilprices of 65 USD/bbl and 70 USD/bbl, therefore no new wells were drilled. With no new wells, the de-cline of the total production is inevitable, which also could be seen in the figure. The production lagsbehind the NPV estimation because it takes (with default settings) two months for an invested well tostart producing, see section 2.2.1. The four simulations shown as green and red curves decline hyperbol-ically starting on February 2015, two month after the first negative NPV estimation. The two remainingsimulations however, shown as blue curves, do not show the same behaviour. For these simulations, theproduction curves decline the first time in March 2015, and the declines of the rest of the simulations arenot hyperbolical. This is because the NPV estimator does not limit the investments with negative NPV,but instead the driling capacity. The decline is therefore caused by decline in the input drilling capacity.



CHAPTER 3. RESULTS

3.2. EXPLORATIVE SCENARIOS

Figure 3.2: Simulated and historical production rate for Eagle Ford play over the period January 2010 toDecember 2015.

3.2 Explorative scenarios

3.2.1 Future Drilling Capacity Kept Constant

What would the future production rate be if the drilling capacity is kept constant, when not using themicro-economic variables? That is what the following section tries to answer. The same setup as insection 3.1.1 was used, except that the number of time steps were doubled so that the simulations continueinto the future. In figure 3.3, two simulations are presented together with historical production rates andthe drilling capacity. These are based on the same historical data as were used in the model evaluationsection. The drilling capacity is kept constant from December 2015 and onwards, using the last valuefrom the historical data.

The resulting production curves in blue and green decline and converge towards constant productionrates of 1.1 million bbl/day and 1.3 million bbl/day respectively. The difference between the two con-verging production rates is significant in the last half of the simulation, this because the simulation withstochastically given curve parameters diverges from the other simulation for each time step.



CHAPTER 3. RESULTS


Figure 3.3: Historical and simulated production rates in black, green and blue curves, together withdrilling capacity as the dashed curve. The drilling capacity is kept constant in the second half of thesimulation, and the resulting production rates converge towards two levels.

The maximum production rates for both the simulations are reached in April 2015, compared with March2015 for the historical data, and are 1.9 million bbl/day respectively 1.8 million bbl/day as monthlyaverage rate for the simulation with stochastically respective deterministically given parameters, seetable 3.1.

Table 3.1: Summary of simulations when the drilling capacity is kept constant.

Max. month Max. production Two years after max. Convergent production“Stochastic” April 2015 1.9 million bbl/day 73 % of max. prod. 1.1 million bbl/day“Deterministic” April 2015 1.8 million bbl/day 68 % of max. prod. 1.3 million bbl/dayHistorical data March 2015 1.7 million bbl/day – –Drilling capacity Oct 2014 510 wells 34 % of max. capacity 172 wells

3.2.2 Future Oil Price Kept Constant

What would the future production rate be if the oil price is kept at a constant value and using NPVcalculations as production constraint? The following section tries to answer ths question. The same setupwere used as in section 3.1.2, i.e. in total six different simulations with the producer strategy ‘NPV rig’,three of them with stochastically given q0,b and D, three of them with deterministically given q0,b andD. And just as in section 3.1.2, costs were set to accommodate break-even points equal to the oil pricesof 50 USD/bbl, 65 USD/bbl, respective 70 USD/bbl. Historical data on oil price, available rigs and wellsper rige rate are loaded and kept constant during the second half of the simulation, starting on January2016.

The resulting production rates are presented in figure 3.4 together with the historical production as blackcurve and the oil price as the dashed curve. All the production rates decrease monotonically after themaximum production rate in February respectively March 2015. Though, they present different declinerates which could indicate on difference in the decline curve parameters d and D in their wells. Theproduction rates do not seem to converge to a constant level.



CHAPTER 3. RESULTS


Figure 3.4: Historical and simulated production rates in black, green and blue curves, together with oilprice as dashed curve. The oil price is kept constant in the second half of the simulation, and the resultingproduction rates converge to two different levels.


4 Discussion

The exploratory simulations on a constant oil price, presented in figure 3.4, shows clearly that no newmaximum of oil production could be reached without a dramatically higher oil price, or dramaticallylower cost levels. There is an ongoing discusison about which oil price represent the break-even point.This break-even point differs for different drilling areas due to factors like cost of drilling, cost of com-pletion, output per rig, availability to pipeline or railroad to transport the oil to the buyers, etc.. See forexample Hilaire et al. (2015), who point out the large difference in shale gas extraction costs and thescarcity of data on these costs. EIA (2016d) forsees a continued downward trajectory in costs as an effectof the the global oversupply of oil and decreased drilling activity. Could this narrow the gap between thethe total cost and the break-even point?

Outlooks for the year 2016 suggested that some producers’ dept reaching roughly four or five times earn-ings before certain expenses (DiChristopher; 2015a). With both negative free cash flow and increasingdept it is unlikely that it will be new investments in the industry (DiChristopher; 2015b). Large invest-ments such as pipelines need stability and certainty of production for a long time ahead, maybe even acouple of decades (Curtis et al.; 2014). But if the oil price is kept at a low level, such investments seemunlikely to be made and the operators have to choose other more costly options for delivering oil.

Is the industry dependent on investment in order to develop? Or will some producers find cost-effectiveoperations and survive, while others will fall? This is what Gevorkyan and Semmler (2016) have tried toanswer. They studied the tight oil producers’ leveraging during the last few years, and how the decreasingoil price affect market shares and excessive borrowing. They affirm that overleveraging and the risk ofinsovency have risen, in particular among the mid- and small- market capitalization (cap) companies.They also suggest that large-cap companies, that previously lost some of their market shares during theboom period when the oil price was high, in the coming few years will likely regain their their dominance.This because of size and access to finance. It is not naive to assume that this dynamics in market shareaffect the total production rate, and both figures 3.3 and 3.4 would look different if this parameter wouldbe included.

In the contrary, EIA (2016a) suggests the production could recover before year 2020, but only in case ofoil price increase or technology advancements. “[P]roduction declines will continue to be mitigated byreductions in cost and improvements in drilling techniques. The use of more efficient hydraulic fracturingtechniques and the application of multiwell-pad drilling, as well as changes in well completion designs,will allow producers to recover greater volumes from a single well” (EIA; 2016a). Decker et al. (2016)noted that even though rig counts have fallen substantially, this has sometimes even-out because of aconsiderable increase in the wells per rig rate. In model presented in this study, the wells per rig rate isincluded as a parameter and runs from 0.8 up to 2.0, see section 2.2.7. But, The cost efficiency is notfacilitated for in the model. “The main driver of this greater rig efficiency is the adoption of pad drilling

16


CHAPTER 4. DISCUSSION

4.1. LIMITATIONS OF THE MODEL

technology whereby a rig can drill multiple wells from the same spot without the need for expensive andtime-consuming disassembly, relocation, and reassembly” Decker et al. (2016). Other trend variablescould be included in future versions of the model to facilitate for decrease in various costs.

In this model, the oil price is chosen to be an exogenous variable. What this model does not take intoaccount is the inter-relationship between the oil supply and the oil price. Manescu and Nuno (2015)analyse the impact of the dramatic increase in shale oil production on oil prices and economic growth.Their findings suggest that the most of the expected increase in oil supply is already incorporated into theprices already. Therefore, to handle the oil price as endogenous and inter-related to for example supplymight just make the model more complex without giving any more results. To make a model, there isalways a balance in the complexity. If the model is too simple, it is not potent enough to simulate andpredict the reality. If it is too complex, it is hard to couple output variations with correct input variations,what Jakobsson et al. (2014) call concreteness.

Whether or not the production will come back to the same levels as of 2014 and 2015, the rest of thesociety will be affected. The tight oil production does not only affect federal politics of import and exportof oil (Grushevenko and Grushevenko; 2012), but also impacts local regions economically. Munasib andRickman (2015) “find large and statistically significant positive effects for the oil and gas counties inNorth Dakota across the entire wide range of regional labor market measures, as well as for the entirenonmetropotitan portion fo the state”Munasib and Rickman (2015).

4.1 Limitations of the model

The simulation with stochastically given parameters has generally higher production rate compared withthe respective simulation with deterministically given parameters. This could be a systematic error in thecreation of the distribution functions or in the allocation of stochastic parameters. More simulations andstudies need to be done to come to a conclusion about this.

The simulated production rate is generally higher than the historical production rate. This means thatthere are other factors that limit the production rate, factors that the model does not include. If the modelhas too many factors, it would be difficult to work with and make any conclusions between input andoutput.

In the ‘NPV rig’ mode, when the NPV estimation limits the production rate, the simulated production islower than the historical production rate. This shows a drawback with the design of the model, which isthe binary “on” or “off” of the production when using ‘NPV rig’. This could be mitigated by, for example,running many producers who are sharing the same drilling capacity but have different break-even points.

The model shows that having constant drilling capacity, the production rate converge towards a constantlevel. The decrease in simulated production rate does not correspond to the decrease in drilling capacity,the decrease in drilling capacity is around two times as large as the decrease in production rate (see table3.1).

4.2 Future Research and Work

The model is built to be modular, flexible and based on clear assumptions. It is easy to swap parts of themodel, change the parameter values, and add new features. This means that there are many more ways in



CHAPTER 4. DISCUSSION

4.2. FUTURE RESEARCH AND WORK

which the model could be employed to explore future than those in this study. It would for example beinteresting to explore the survivability of producers. This by using the producer strategy ‘NPV cash rig’and giving the producer a initial capital. Many producers could be given different settings of costs andtherefore lower or higher responsiveness to the oilprice. After this setup, different scenarios of future oilprice could explore the behaviour of the producers. For example their ability to retain capital in periodsof low oil price.

Future work could also be to explore impacts of technological development and depletion of drillingsites by using trends in for example q0, drilling capacity, costs, available drilling locations. Furthermore,company production strategies and properties (credit size etc.) could be explored under different pricescenarios.

The aim of this study was not to find and implement accurate or optimal parameter values. When moreaccurate parameter values are found, for example various costs used in the model, the values in the modelare easy to update and better results could be produced.


5 Conclusion

By using a bottom-up well-by-well approach which includes the geological and physical nature of awell along with micro-economic principles, a model is implemented and evaluated. The model is mademodular, flexible and the parameters and units are grounded in concrete practicalities.

Simulation results replicate the historical production profile of Eagle Ford play both when only usinghistorical drilling capacity and when using both drilling capacity and NPV estimation as the independentvariables. The model could also present explorative results. The model shows that when the drillingcapacity was decreased with 67 %, the production rate decrease with around 30 %. The model alsoshows that when the oil price is kept at a constant low level the production rate could decline with ahyperbolical fashion.

19

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Appendices

A Decline Curves

Figure 1: Hyperbolic decline curves of the production rate of a single horizontal oil well. Different valuesof first parameter b changes the shape of the decline curve.

Figure 2: Hyperbolic decline curves of the production rate of a single horizontal oil well. Different valuesof the second parameter D changes the shape of the decline curve.

24


BIBLIOGRAPHY

B. THE EFFECT OF WELLS PER RIG RATE

B The Effect of wells Per Rig rate

Figure 3: The effect of the variable wells Per Rig rate on simulated production. Plotted is simulated alongwith historical production rate from January 2010 to December 2015.

C Histogram of Decline Curve Parameters

Figure 4: Histograms of the hyperbolic decline parameter b. Source: Lund et al. (2015).



BIBLIOGRAPHY

C. HISTOGRAM OF DECLINE CURVE PARAMETERS

Figure 5: Histograms of the hyperbolic decline parameter D. Source: Lund et al. (2015).


techno-economic modelling of tight oil production1090802/... · 2017. 4. 25. · modelling of tight...

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