Impacts of Hydraulic Fracturing and Completion Technology ...rpatrick/CCP2.pdf · Impacts of...

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Impacts of Hydraulic Fracturing and Completion Technology on Shale Gas Well Production1

Janie M. Chermaka James W. Craftonb Robert H. Patrickc

University of New Mexico Performance Sciences, Inc. Rutgers University

Revised August 2015 May 2013

Abstract

We estimate early period shale gas cumulative production functions and output elasticities for vertical and horizontal well technologies. Results indicate reservoir characteristics and completion outcomes have significant impacts that are consistent in sign across the two technologies, but the magnitudes and probabilities of these impacts vary, sometimes substantially so. The impacts of completion decisions on cumulative production are highly variable, with differences in early period production declines across the two technologies. These results may, in part, explain the downward trend in reserve estimates for shale gas, as there is uncertainty in the impact of completion choices on early period production. KEYWORDS Production function, technology, shale gas, hydraulic fracturing, output elasticities CLASSIFICATION CODES Q4 (energy), Y8 (Related Disciplines), L7 (Industry Studies: Primary Products), L71 (Mining, Extraction, and Refining: Hydrocarbon Fuels), D (Microeconomics), D24 (Production) C3 (Simultaneous Equations), C33 (Models with Panel Data)

1 We’d like to thank Alan Krupnick, Tim Fitzgerald, and other participants at the 2011 and 2012 USAEE/IAEE North American Conferences, David Lamont and other participants at the Rutgers University CRRI Advanced Workshop and Regulation and Competition, 31st Annual Eastern Conference for helpful comments on previous versions of this paper. Chermak and Patrick thank PSI for partial financial support. a Department of Economics, University of New Mexico, MSC05 3060, 1UNM, Albuquerque, NM 87131: [email protected] b Performance Sciences, Inc., Evergreen, CO 80439 c CORRESPONDING AUTHOR; Finance and Economics, Rutgers Business School–Newark and New Brunswick, Rutgers University, 1 Washington Park 1148, Newark, New Jersey 07102. Email: [email protected].

Chermak, Crafton, and Patrick

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1.0 INTRODUCTION

Shale gas production is a relatively recent entrant into the natural gas industry. While the

potential of shale gas had been known for some time, advancements in technology have allowed

the use of hydraulic fracturing, directional and horizontal drilling, and reservoir evaluation

methodologies resulted in the ability to exploit these reserves. This was once a phenomenon

largely confined to the US energy industry, but is gaining interest throughout the world. For

example, China and Canada are now producing shale gas as wellIt should be noted, however,

that other countries are proceeding more slowly.

Shale gas is not without controversy, which may be one of the reasons for the caution

shown. In addition to the well-publicized debate over potential environmental effects of

hydraulic fracturing and the amount of water needed to complete a well,2 there is significant

uncertainty concerning the quantity of actual reserves. For example, between 2011 and 2012, the

US Energy Information Administration (EIA) reduced its estimate of unproved technically

recoverable resource for the US by almost half in its Annual Energy Outlook (AEO)..3 In part

this was due to early period production decline (which impacts ultimate recovery) that was far

greater than originally expected (EIA 2012, 2011).

Ultimately, the impact of shale gas on the natural gas industry and its contribution to the

long-term viability of the industry will depend on actual production meeting forecasts and

estimated ultimate recovery (EUR). As with any natural gas resource, well performance depends

2 Although the US Environmental Protection Agency (2015) issued a draft assessment of the potential impacts of hydraulic fracturing and found no evidence of “widespread systematic impacts on drinking water resources in the United States,” the debate appears far from over. 3 Unproved technically recoverable reserves are defined as reserves estimated to be commercially recoverable in the future from known reservoirs and under current economic conditions, operating methods, and government regulations, but have not been proven to exist based on accepted geologic information.


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not only on the characteristics of the well and the reservoir, but also on choices made by the

producer; completion, production, and recompletion choices. In the case of shale gas wells, this

may be even more important as some work suggests early production management decisions and

can significantly impact EUR (Crafton 2008). Consequently, a better understanding of the

impact of reservoir and completion characteristics on early period production, and the impact on

economic vitality is of importance. Included in this is the consideration of vertical versus

horizontal well performance. While horizontal wells may have substantially larger initial

production levels than vertical wells, this is a newer technology with greater uncertainty of

ultimate recovery.

This paper provides estimates of the impacts of shale well completion and production

decisions on early-period natural gas production, controlling for well characteristics, for vertical

and horizontal well technologies. Employing data from 111 (39 horizontal and 72 vertical) shale

gas wells, we econometrically estimate a system of equations for early period cumulative

production conditional on discrete inputs into fracturing and completion of the well. We find

reservoir characteristics and completion outcomes are statistically significant but vary

substantially in magnitude across the vertical versus horizontal well technologies. Further, we

find the cumulative production elasticities are variable both in sign and magnitude across the two

technologies.

2.0 BACKGROUND

Natural gas was first produced commercially in the US in 1821. Initially production was

from “conventional” reservoirs. That is, onshore, sandstone reservoirs which are characterized

by high porosity and permeability, allowing gas in the reservoir and conduits to flow through the

reservoir. Over time, improved technology allowed economic production from increasingly


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challenging plays, including offshore reservoirs, tight formations (low porosity and

permeability), coalbed methane, and more recently, shale.

Shale gas production is unique in that the gas is in situ in the source rock and has not

migrated to a reservoir. While the existence of deep shale gas resources was known by at least

the 1980’s, the economic conditions didn't exist to pursue the technology and/or the plays. In the

early 1990’s, Mitchell Energy began utilizing hydraulic fracturing in the Barnett shale in Texas

to stimulate production and then incorporated horizontal drilling to potentially increase the yield

from a well.

Shale gas reserves are an unconventional resource where the gas is located in very low

permeability geologic formations. Large estimated reserves in the US are located in the

Devonian-age Marcellus shale in the northeast, the Jurassic-age Haynesville shale in the south,

the Cretaceous-age Eagle Ford shale in the south, and the Missipian-age Barnet in Texas. Low

permeability makes movement of the gas difficult, which precipitates the need for appropriate

technology to be able to move the gas through the reservoir to the well, and finally to the earth’s

surface. The combination of technologies pioneered by Mitchell Energy enabled the production

of this resource. Coupled with the drilling and completion technology, reservoir evaluation is a

necessary component of shale gas production. The US Securities and Exchange Commission

(SEC) recognized this need by publishing Release 33-8995 (SEC 2009), in which they identify

the requirements for improved evaluation procedures. This has been further documented in

technical papers (e.g., Lee 2010). In this study, one of the evaluation tools satisfying the SEC

requirements was employed for the evaluation of reservoir quality and stimulation effectiveness

(Crafton, 1997).


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The impact of shale production on the natural gas industry is substantial. In 2000, less

than 0.4 TCF of natural gas production in the U.S. was from shale gas reserves. By 2013 more

than 11.8 TCF of natural gas production was from shale gas. While the total production

contribution from shale is forecast to increase in the long run, the EIA (2015) is projecting a

short-term decline in shale production for the first time since its been tracked due to production

declines in existing wells not being offset by new wells coming on line. This illustrates the

importance of improving efficiency, especially in times when natural gas prices are low.

After a well is drilled, a completion plan is made. The plan can include, among other

things, the interval to be perforated, the amount and type of hydraulic fracturing fluid and

proppant to be injected into the reservoir, the speed with which the hydraulic fracturing fluid is

introduced and the number of stages (the number of completion intervals) – all of which will

result in a conduit being formed through the reservoir, providing a path for the gas to move from

the reservoir to the wellbore and finally to the surface.

Perforations are holes shot through the well casing in order to make a connection between

the wellbore and the reservoir rock. Hydraulic fracturing fluid is then injected at pressure to

propagate fractures or fissures through the reservoir rock to the wellbore. Proppant is a material

that keeps the fracture open and provides a conduit for the flow of gas to the wellbore.

Figure 1 illustrates the vertical and horizontal technologies. In both cases, the wells have

the same shale zone of interest and the fractures are propagated out from the casing. Given the

position of the casing, the vertical well results in fractures relatively parallel to the length of the

formation, while the horizontal well results in fractures perpendicular to the thickness of the

formation. The length of a fracture is dependent on a number of factors, including the

characteristics of the host rock and, as discussed earlier, the completion job chosen. A


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characteristic of the horizontal technology is that the horizontal portion of the well is drilled

parallel to the zone of interest, resulting in the potential for a number of stages, while the vertical

technology is limited by the thickness of the zone.

FIGURE 1: Vertical versus horizontal wells

The completion decisions include the volume of fluids, the pressure under which the fluids

are delivered, the characteristics of the proppant, as well as the additives to include in the fluid.

The additives can include, for example, corrosion and scale inhibitors, biocides, and surface

active agents. The surface active agents, which help reduce the surface tension, can include

surfactants or a Complex nano-Fluid (CnF). The composition of these additives varies and

historically were often proprietary and may not have been environmentally benign.4 With the

negative publicity from hydraulic fracturing fluid, there has been a push by industry to reduce the

environmental footprint of these various additives. This can, in itself, become a completion

4 As of 2015, 29 states now require some type of hydraulic fracturing chemical disclosure (fracfocus.org: Last accessed 08//30/2015).


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choice.5 For example, CnF is relatively environmentally benign and has the distinction that

when used in the North Sea, in the case of a spill, it is classified as a non-environmental event.6

These completion decisions made by the company are, in part, based on the

characteristics of the reservoir, but also may depend on a company’s management styles and

policies, as well as on those of the completion company.

While shale gas reservoirs are substantially different than other unconventional natural

gas reservoirs, initially the standard industry practices were used in their production, i.e., they

followed that of other reservoirs - mainly high flowback (production of the fracturing fluid) and

production rates. Discussions have emerged about what constitutes the optimal completion and

production plan for a shale gas well (Crafton 2008). Fracture length, number of stages, fracture

conductivity, and production pressure chokes have all come into play (Crafton 2011). While the

initial capital investment may be increased and the time to payout extended due to lower initial

production rates, the overall profitability of a well can be improved if total revenues are

increased over the life of the well due to increased production and/or reduced capital costs over

the life of the well due to lower initial expense and fewer work-overs of the well.7 For example,

Petrohawk Energy has employed more conservative production plans in the Haynesville,

producing wells on more restrictive chokes (15/64 or 16/64 inch choke) and reported decreases

in decline rates.8,9

5 For example, the US Environmental Protection Agency held a workshop in February 2011, in which industry representatives presented the changes that are being made to reduce potential environmental degradation. 6 Certified by the Center for Environment Fisheries and Aquatic Science, Department of Energy and Climate Change, State Supervision of Mines, Ministry of Economic Affairs, UK. 7 This is consistent with Patrick and Chermak (1992) and Chermak et al. (1999) on producing other alternative natural gas sources, discussed further below. 8 A production choke is a flow control device that limits the flow of natural gas. 9 Petrohawk Q3 2010 Results: Earnings Call Transcript (11/02/2010).


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In terms of the economics literature addressing shale gas, Fitzgerald (2013) discusses

economic factors in hydraulic fracturing, including environmental impacts. Lake, et al., (2013)

provide a case study of a single well, developing a model to evaluate the economic viability of a

shale gas well. They conclude that most shale gas wells are profitable (under their modeling

assumptions). Hefley and Wang (2014) provide additional details and shale gas case studies.

Fitzgerald (2015), discussed further below, analyzes hydraulic fracturing data for wells in North

Dakota and Montana, and finds, among other things, that producers benefit from experience.

Otherwise, previous economic studies have focused on the optimal completion and production of

other types of unconventional gas resources, in particular, vertical technology, tight sand

resources. Chermak and Patrick (1995a), among other things, estimate well-level cost functions

for tight sand natural gas wells. Chermak and Patrick (1995b) estimate the value of information

technology in tight sand gas production. Patrick and Chermak (1992) and Chermak et al. (1999)

develop hybrid economic-engineering models for optimal tight sand natural gas well fracturing,

completion, and production. They show how fracture length affects well profitability, among

other things, and find that larger fractures are not necessarily the most profitable. In this paper

we contribute to this literature by analyzing a unique sample of shale natural gas wells, including

the relatively new horizontal drilling technology, as well as traditional vertical technology wells.

The completion and production of a well involve a number of interdependent decisions.

We are interested in the developing the empirical representations of the interdependencies

between the components of completion and production geology and technology, i.e., how

decision variables affect the fracture and conductivity (capital investments) and how these in turn

affect cumulative production. We model the physical interdependency through a series of

interdependent production functions representing the completion and production of the well.


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These interdependent physical relationships are a necessary input to determine the economically

optimal completion and production of the natural gas well (i.e., to maximize the value of the

resource).

3. 0 MODELING FRAMEWORK

We adopt a relatively simple representation of a shale gas production function.10 The

natural gas production function for well i = 1,..., I , at time t is given by

qit = h Ai0,Ki0,Fi0,Ci0( )θit (1)

where θit = Beηi+ut+vit with ηi and ut , unobserved well and time specific effects, and vit is a

random component. Factors impacting production include physical attributes of the reservoir,

Ai0 = A1i0,...,Ani0( ), i = 1,..., I , which can also impact reserves; completion (input) production

functions, Fi and Ci ; and production qit through the choices Ki0 = K1i0,...,KMi0( ), i = 1,..., I;

that impact productivity either through reserves or feedback. The logarithmic specification of

the production function is

lnqit = βA lnAi0 + εKq( )it lnKi0 + εF

q( )it lnFi0 + εCq( )it lnCi0 + lnθit (2)

where ε jq( )it is the output elasticity for factor

j = K , F ,C( ) and βA is a parameter (elasticity)

associated with the logarithm of exogenously fixed variables A. Analogous to Solow (1957),

firms are assumed to maximize profits given input and output prices, i.e., they are price takers in

input and product markets. At time t = 0 , all factors that the firm controls in terms of fracturing

and completing the well are variable, so profit maximization implies the first-order conditions

10 Caputo (2010) considers, among other things, continuous capital investment in production of exhaustible natural resources. See his paper for a review of the capital literature in this regard. In this paper we consider a simple model of discrete capital investment (e.g., fracturing) in pressure driven exhaustible resources such as natural gas.


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ε jq( )it = β j( )it , for all j (3)

where β j( )it =wjit jitPitqit

, the share of factor j costs in total revenue, and ji0 and qit are at their profit

maximizing levels.11 This implies that factors are paid the value of their marginal product.

Since firms are producing an exhaustible resource, profit maximization implies qit is determined

so that the sum of marginal cost, cqit , and the shadow (option) value of the exhaustible resource,

λit , will equal the contemporaneous product price. 12 So the ratio

Pit

cqit + λit

= 1. Substituting

conditions (3) into (2), we have the form of the relationship we estimate,

lnqit = βA( )it lnAi0 + βK( )it lnKi0 + βF( )it lnFi0 + βC( )it lnCi0 + lnθit (4)

F and C, the fracture and conductivity of the well, respectively, are not directly observed, so they

are estimated. F and C each require discrete inputs initially so that natural gas can be produced

from the well. Therefore we estimate a system of equations that includes sub-production

functions for F and C.

Cumulative production at time t is then given by integrating (1),

Qit = qi x( )0

t

∫ dx = h Ai0,Ki0,Fi0,Ci0( )θix0

t

∫ dx. (5)

From the perspective of estimating cumulative production, the discrete factors are fixed as they

are chosen initially. For notational ease, we abstract at this point from the fact that not all

characteristics or inputs are of relevance in each of the discrete production functions. Since

11 We directly estimate the production function to obtain the factor output elasticities reported below. Mairesse and Jaumandreu (2005) find very little difference in results from estimating the production function (using a real output) versus a revenue function (using price index weighted output). 12 This follows from Hotelling (1931). Chermak and Patrick (2001, 2002) summarize tests of Hotelling and, analyzing a panel data set for 29 tight-sand gas wells, find that producer behavior is consistent with Hotelling’s exhaustible resource theory.


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cumulative production is dependent on the endogenous variables Fi and Ci , we estimate

cumulative production simultaneously with specifications of the fracture production function and

the fracture conductivity production function. Explanatory variables that are in Qit and Fi

and/or Ci will have both direct and indirect effects on cumulative production, Qit , e.g.,

∂Qit ∂Kim0 =

0

t

∫ (∂hit ∂Kim0

direct + ∂hit ∂Fim0 ∂Fim0 ∂Kim0

indirect + ∂hit ∂Cim0 ∂Cim0 ∂Kim0 )

indirect dx (6)

The specified average production functions comprising the system of equations that we

estimate are then given by

lnQit = β0 + β jj=1

M

∑ lnKij0 + β j lnj=1

n

∑ Aij0 + βF lnFit0 + βC lnCit0 + β jj∑ Dijt + e1it (7)

lnFi0 =δ0 + δ jj∑ lnKij0 + δ j ln

j∑ Aij0 + δ j

j∑ Dij0 + e2it (8)

and

lnCi0 = γ0 + γ jj∑ lnKij0 + γ j ln

j∑ Aij0 + γ j

j∑ Dij0 + e3it , (9)

where the β, δ and γ ' s in each equation are the parameters to be estimated, the D variables are

binary variables and time fixed effects, and only subsets of the A, K, and D variables are in each

equation, with some of the subset of elements mutually exclusive (the equations are completely

specified with the estimates below). Time fixed effects are used to capture unobserved

heterogeneity over time. We expect these effects to approximate the impact of unobservable

production decisions and pressure declines on cumulative production.

Equations (7), (8), and (9) comprise the empirical system of average production and sub-

production functions we estimate. The expected cumulative production for well i at time t can


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then calculated as follows. Denote the expectations of (8) and (9), conditional on all information

available at time t, E lnFi0( ) = lnFi0 and E lnCi0( ) = lnCi0 , respectively. Substitute these

conditional expectations into the expected value of (7), conditional on all information available

at time t, and apply the exponential operator to obtain the predicted average cumulative

production for well i at time t, i.e.,

Q̂it = exp β̂0 + β̂ j

j=1

M

∑ lnKij0 + β̂ j lnj∑ Aij0 + β̂F lnFi0 + β̂C lnCi0

+ β̂ jj∑ Dijt

⎛

⎝⎜⎞

⎠⎟ (10)

4.0 DATA

The data are from 111 shale gas wells located in the US. Due to producer confidentiality,

the locations are not revealed. However, the data are all from a single basin and a single geologic

setting. Thus heterogeneity across basins is not a consideration in the study.

There are 39 horizontal wells and 72 vertical wells in our sample. All of the wells have

been completed and production initiated since 2007. We categorize the vertical and horizontal

technologies sample data by production, reservoir or well characteristics, completion choices,

and completion outcomes. Naturally, some, but not all, variables are applicable across both the

vertical and horizontal technologies.

Well characteristics include permeability thickness13, initial reservoir pressure14, and the

perforated interval (to proxy for reservoir thickness – included for vertical wells, but not for

horizontal due to the lack of variation in the data for horizontal).

13 The product of reservoir permeability times thickness of the reservoir. 14 The hydrostatic pressure of the formation prior to first production.


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Completion choices include the quantity of hydraulic fracturing fluid, proppant quantity

per stage and proppant concentration (pounds per barrel of hydraulic fracturing fluid), and the

concentration of the surface active agent (gallons of additive relative to total gallons of fluid).

In the case of the vertical wells, all wells were treated with CnF at varying

concentrations. For horizontal wells, three were treated with CnF and the remaining 36 wells

were treated with a variety of traditional surfactants. We test the statistical significance of

traditional surfactants versus CnF in the horizontal wells, distinguishing the CnF wells using

intercept and interactions terms. One interest in comparing the impacts of the traditional

surfactants versus CnF is due to the environmental aspects of CnF.

We also consider the choice of the number of stages for the horizontal wells (all vertical

wells have only a single stage). Because summer versus winter temperature differentials may

impact the completion outcome, we include a binary dummy for winter completion jobs as a

completion choice variable.

In addition, the injection rate and resulting average treatment pressure is included for

vertical wells, while only the injection rate is included for horizontal wells (lack of variation in

treatment pressure precludes its inclusion for the horizontal wells). Because the speed with

which a completion job in finished may impact production, we include the time between the

beginning of the completion job and first production.

Completion outcomes include final and early fracture half-lengths and normalized

fracture conductivity.15 Finally, we consider the impact of time on cumulative production

through two variables. First, we include a ratio of production days to total calendar days to

15 Fracture conductivity, which measures how easily fluids move through a fracture, is the product of fracture permeability and fracture width. We utilize a more common dimensionless fracture conductivity, equal to fracture conductivity divided by the product of final fracture half-length and formation permeability, which accounts for differences in reservoir characteristics.


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produce those production days.16 Second, there are at most seven cumulative production periods

for each well; the first ten days, then 30, 60, 90, 180, 360, and 720 days. The 720 days of

production are only applicable to some of the horizontal wells in our sample. Thus we have

incremental production for up to 12 months for our vertical well data set and up to 24 months for

our horizontal well data set. Table 1 provides a dictionary for our sample data for each well.

TABLE 1: Variable Names, Descriptions, and Units Variable Description Units Cumulative Production i (i =10, 30, 60, 90, 180, 260, 720 days)

Cumulative Production to a point in time Thousand cubic feet (MCF)

Final Fracture Half-length Effective final fracture from wellbore Feet Dimensionless Fracture Conductivity

Product of fracture permeability and propped fracture width divided by the product of fracture half-length and formation permeability

Unitless

Initial Reservoir Pressure Pressure prior to completion and production Pounds per square inch (PSIG)

Permeability thickness Reservoir permeability * reservoir thickness millidarcy feet Perforated Interval Range of reservoir perforated Feet Early Fracture Half-length Effective early period fracture length from wellbore Feet Proppant Concentration Pounds of proppant divided by gallons of hydraulic

fracturing fluid Pounds per gallon

Average Pounds of Proppant per stage

Pounds of proppant used in completion divided by the number of stages

Pounds

Surfactant Concentration (horizontals) or CnF Concentration (verticals)

Percentage fluid that is a surface active agent additive (scaled by 100)

Percent*100

Stages Number of stages used for the completion Numeric (1,2,3) Average Injection Rate Rate at which fluids are injected Barrels per minute Average Treatment Pressure Average pressure used for injection Pounds per

square inch Difference Difference in Days between beginning of

completion job and day of first production Days

Ratioi (i =10, 30, 60, 90, 180, 260, 720 days)

Ratio of total days of production to total calendar days necessary to achieve the days of production (scaled by 100)

Percent*100

Days i (i =10, 30, 60, 90, 180, 260, 720 days)

Cumulative days of production Days

16 For example, if we were interested in one day (24 hours of production) and a well was produced for 12 hours each day for two consecutive days, the ratio would be ½. We include the ratio to test for the impact of inactivity on cumulative production.


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Descriptive statistics for the data are provided in Table 2.17 Based on the above

discussion, the specified variables across the models are not identical. Of note are the

differences in the average cumulative production between the vertical and horizontal wells. The

first ten days production for the horizontal average is almost three times that of the vertical

average cumulative production. This relatively large production is a reason for the immense

interest in the horizontal technology.

17 Wells refers to the number of wells on which the statistic is based. Note later production periods have smaller numbers of wells because all wells do not have the same production periods.

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TABLE 2: Descriptive Statistics

Variable Vertical Horizontal Mean s.d. Min Max Wells Mean s.d. Min Max Wells

Well Characteristics Permeability Thickness 0.82 0.93 0.14 6.77 72 3.08 4.44 0.04 18.68 39 Initial Reservoir Pressure 4703.86 268.48 2609.50 5015.80 72 5071.60 843.93 2892.58 8073.99 39 Perforated Interval 72.86 21.04 40.00 129.00 72 n.a. n.a. n.a. n.a. n.a.

Completion Outcome Final Fracture Half-length 48.32 18.12 15.96 109.55 72 122.47 96.71 3.54 419.54 39 Early Fracture Half-length 40.29 18.78 2.67 80.12 72 163.72 135.12 3.54 655.03 39 Dimensionless Fracture Conductivity 1790.28 1415.14 150.00 6038.00 72 2808.57 5759.69 36.99 29400.40 39

Completion Choices Average Pounds Proppant per Stage 945592 233178 369000 1256600 72 597728 243522 59600 1018010 39 Proppant Concentration 0.85 0.18 0.50 1.32 72 1.18 0.52 0.17 3.21 39 Surfactant Concentration n.a. n.a. n.a. n.a. n.a. 0.09 0.07 0.00 0.37 39 CnF Concentration 0.12 0.06 0.02 0.22 72 n.a. n.a. n.a. n.a. n.a. Average Injection Rate 109.11 19.52 44.70 134.20 72 71.82 14.88 15.65 89.71 39 Average Treatment Pressure 5903.83 552.55 3868.00 7307.00 72 5867.71 1140.82 3486.00 8170.07 39 Stages n.a. n.a. n.a. n.a. n.a. 6.44 3.73 1.00 15.00 39 Winter Fracture 0.29 0.46 0.00 1.00 72 0.33 0.48 0.00 1.00 39 Difference 7.67 8.05 2.00 36.00 72 6.92 23.63 0.00 150.00 39

Production Cumulative Production 10 6096 5595 174 30330 72 18029 11395 1290 43820 39 Ratio 10 Days 98.29 8.42 43.48 100.00 72 58.38 39.41 2.87 100.00 39 Cumulative Production 30 15113 12825 855 78598 72 63526 42755 7069 181871 35 Ratio 30 Days 97.28 10.77 35.29 100.00 72 42.14 30.26 7.87 100.00 35 Cumulative Production 60 25123 20668 1342 131111 72 126762 91813 15437 428638 32 Ratio 60 Days 98.02 7.95 51.28 100.00 72 52.25 27.87 14.47 100.00 32 Cumulative Production 90 33183 27540 1736 174876 72 182450 118871 19635 379801 27 Ratio 90 Days 98.48 6.32 60.40 100.00 72 60.01 24.28 20.19 99.68 27 Cumulative Production 180 51730 44357 2794 296232 70 292418 229082 30251 764838 20 Ratio 180 Days 98.74 5.55 59.02 100.00 70 68.57 21.98 32.33 99.84 20 Cumulative Production 360 78478 47821 24929 172550 20 384113 432480 47738 1426010 11 Ratio 360 Days 99.02 2.75 87.80 100.00 20 80.24 17.05 51.37 99.92 11 Cumulative Production 720 n.a. n.a. n.a. n.a. n.a. 262897 320541 76980 633025 3 Ratio 720 Days n.a. n.a. n.a. n.a. n.a. 85.43 12.09 72.23 95.96 3

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5.0 RESULTS

Based on (7), (8) and (9), we consider cumulative production within the first two years of

production for our sample of shale gas wells from the US. Our systems of equations for vertical

and horizontal wells consist of three equations each:

EQ1: Cumulative Production (Q) is a function of: − Well Characteristics (A; initial reservoir pressure, permeability thickness, perforated

interval for the vertical wells).18 − Completion choices (K; difference between start of completion job and first production,

and winter fracture) o Specific to vertical wells (CnF concentration) o Specific to horizontal wells (Surfactant concentration, CnF intercept and

interaction) − Completion outcomes (F, fracture half-length (late) and C, dimensionless fracture

conductivity) − Time (D; ratio of production days to calendar days and intervals (30 days, 60 days, etc.)

EQ2: Final Fracture Half-length (F) is a function of: − Well Characteristics (A; initial reservoir pressure and permeability thickness). − Completion outcome (F, early fracture half-length) − Completion Choices (K; average pounds of proppant per stage, average injection rate and

winter fracture). o Specific to the vertical wells (average treating pressure and CnF concentration). o Specific to horizontal wells (number of stages, surfactant concentration, CnF

intercept and interaction) EQ3: Dimensionless fracture (C) conductivity is a function of:

− Well Characteristics (A; initial reservoir pressure and permeability thickness). − Completion choices (K; proppant concentration)

o Specific to the vertical wells (average treating pressure, CnF concentration). o Specific to horizontal wells (number of stages, surfactant concentration, CnF,

intercept and interaction).

With the exception of the binary variables for winter fracture, the CnF intercept for the

horizontal wells, and the time effects for days of production, all variables are transformed by

taking the natural logarithm.

We estimate the system of equations for vertical wells and for horizontal wells separately.

3SLS is used to simultaneously estimate the systems of equations for each technology. Table 3

18 Although perforated interval could be classified as a production choice, we specify it as a proxy for reservoir thickness because it is based on the thickness of the productive interval. Regardless, the classification will not impact our econometric results.


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presents the estimated parameters, and their standard errors, probabilities, means for vertical

wells, and specification tests for each equation in the system. Table 4 contains the analogous

estimated model for horizontal wells.

TABLE 3: Vertical Well Results Equation 1: ln(Cumulative Production)=

Variable Coefficient SE Probability Mean of X Ln Initial Reservoir Pressure 3.0431 0.4142 0.00 8.45 Ln Permeability Thickness 0.9554 0.2150 0.00 -0.56 Ln Perforated Interval 0.0748 0.0682 0.27 4.25 Ln Final Fracture Half Length 0.5087 0.0884 0.00 3.82 Ln Dimensionless Fracture Conductivity 0.4326 0.2188 0.05 7.16 Ln CnF Concentration 0.0624 0.0274 0.02 -2.25 Ln Difference -0.2205 0.0346 0.00 1.73 Ln Ratio 0.5411 0.2177 0.01 4.58 Winter Fracture 0.0413 0.0424 0.33 0.31 30 Days 1.0372 0.0492 0.00 0.19 60 Days 1.5764 0.0491 0.00 0.19 90 Days 1.8613 0.0491 0.00 0.19 180 Days 2.3229 0.0495 0.00 0.19 360 Days 2.7141 0.0765 0.00 0.05 Constant -24.2175 4.6091 0.00

Equation 2: ln(Final Fracture Half-length)= Variable Coefficient SE Probability Mean of X Ln Initial Reservoir Pressure -0.1825 0.1855 0.33 8.45 Ln Permeability Thickness 0.1057 0.0186 0.00 -0.56 Ln Average Treating Pressure -0.2898 0.1628 0.08 8.68 Ln Early Fracture Half-length 0.3472 0.0269 0.00 3.55 Ln Injection Rate 0.0526 0.0820 0.52 4.66 Ln Proppant 0.0033 0.0565 0.95 13.72 Ln CnF Concentration 0.0061 0.0205 0.77 -2.25 Winter Fracture 0.0323 0.0285 0.26 0.31 Constant 6.4118 1.9080 0.00

Equation 3: ln(Dimensionless Fracture Conductivity)= Variable Coefficient SE Probability Mean of X Ln Initial Reservoir Pressure -1.4234 0.0933 0.00 8.45 Ln Permeability Thickness -1.0296 0.0079 0.00 -0.56 Ln Average Treating Pressure -0.3847 0.0749 0.00 8.68 Ln Proppant Concentration 0.2536 0.0363 0.00 -0.17 Ln CnF Concentration -0.0071 0.0105 0.50 -2.25 Winter Fracture -0.1485 0.0142 0.00 0.31 Constant 22.0255 0.8700 0.00

378 observations RMSE "R2 " χ2

(df) Hausman (df) Equation 1: 0.295 0.93 5,240.70 (14) 19.68 (2) 7.11 (7) 3.79 (5) Equation 2: 0.235 0.61 562.43 (8) 3.72 (8) Equation 3: 0.120 0.98 18,431.78 (6) 11.65 (6)

χ2 degrees of freedom (in parentheses). Hausman specification tests for 1st equation restrictions are the endogenous

variables, the exogenous variables, and the time effects, respectively.


19

TABLE 4: Horizontal Well Results Equation 1: Ln(Cumulative Production)=

Variable Coefficient SE Probability Mean of X Ln Initial Reservoir Pressure 0.0311 0.2280 0.89 8.51 Ln Permeability Thickness 0.6459 0.1377 0.00 -0.06 Ln Final Fracture Half Length 0.3540 0.0459 0.00 4.58 Ln Dimensionless Fracture Conductivity 0.2134 0.1347 0.11 6.43 Ln Surfactant Concentration -0.0540 0.0161 0.00 -6.12 CnF Intercept 15.5696 3.2559 0.00 0.12 Ln CnF Interaction 2.3360 0.4795 0.00 -0.81 Ln Difference -0.1240 0.0339 0.00 0.93 Ln Ratio -0.0009 0.0414 0.98 3.80 Winter Fracture -0.1079 0.0775 0.16 0.34 30 Days 1.2483 0.0881 0.00 0.21 60 Days 1.8456 0.0908 0.00 0.19 90 Days 2.1760 0.0965 0.00 0.16 180 Days 2.7238 0.1083 0.00 0.12 360 Days 3.2311 0.1352 0.00 0.07 720 Days 3.4582 0.2390 0.00 0.02 Constant 6.1013 2.4231 0.01

Equation 2: Ln(Final Fracture Half-length)= Variable Coefficient SE Probability Mean of X Ln Initial Reservoir Pressure 1.3838 0.2104 0.00 8.51 Ln Permeability Thickness 0.0227 0.0603 0.71 -0.06 Ln Early Fracture Half-length 0.7568 0.0439 0.00 4.80 Ln Stages 0.5373 0.1162 0.00 1.57 Ln Average Injection Rate 0.6683 0.1331 0.00 4.24 Ln Average Proppant per Stage -0.5129 0.2802 0.07 5.73 Ln Surfactant Concentration 0.0769 0.0161 0.00 -6.12 CnF Intercept -0.6211 3.2125 0.85 0.12 Ln CnF Interaction -0.1650 0.4744 0.73 -0.81 Winter Fracture 0.0743 0.0781 0.34 0.34 Constant -11.1769 2.0457 0.00

Equation 3: ln(Dimensionless Fracture Conductivity)= Variable Coefficient SE Probability Mean of X Ln Initial Reservoir Pressure 0.0007 0.0007 0.35 8.51 Ln Permeability Thickness -1.0003 0.0001 0.00 -0.06 Ln Stages 0.0007 0.0003 0.01 1.57 Ln Proppant Concentration 0.9987 0.0004 0.00 6.38 Ln Surfactant Concentration 0.00004 0.0001 0.43 -6.12 CnF Intercept 0.0072 0.0097 0.46 0.12 Ln CnF Interaction 0.0011 0.0014 0.46 -0.81 Winter Fracture 0.0008 0.0002 0.00 0.34 Constant 0.0017 0.0074 0.82 167 observations

RMSE "R2 " χ2 (df) Hausman (df)

Equation 1: 0.3852 0.92 2,023.53 (16) 7.83 (2) 6.35 (8) 4.72 (6) Equation 2: 0.3787 0.84 889.67 (10) 6.77 (10) Equation 3: 0.0012 0.99 3.77E+08 (8) 2.81 (8)

χ2

df=degrees of freedom (in parentheses). Hausman specification tests for 1st equation restrictions are for the endogenous variables, the exogenous variables, and the time effects, respectively.


20

As in most empirical analyses, we’d like to have more data through time, cross-section

and the range of explanatory variables available. Nonetheless, we want to understand what we

can from the data available. Recognizing that there may be omitted variables or other problems

that can affect the consistency of our estimated parameters, we provide a number of Hausman

specification tests as checks.19 For horizontal cumulative production, equation 1, the test statistic

is 7.83 with 2 degrees of freedom (for the specified endogenous variables on the right hand side

of 1), so the null hypothesis (of model misspecification) is rejected at the usual levels of

significance. This specific application of the Hausman test supports the model specification with

“final fracture half-length” and the “dimensionless fracture conductivity” variables as

endogenous. Analogous results hold for the vertical technology specification. For both

technology models, we also test the exogeneity of the variables specified as exogenous in each

equation. This provides two additional tests for the first equation in each of the systems; a test of

the subgroups of the explanatory variables and a test of the subgroup of time effects. All of these

tests support the consistency of the estimated parameters in our specified models.

Regarding the estimated parameters, we find statistically significant direct impacts for

both models across each of the three equations in the system. For example, statistically

significant (at 90% or better) direct impacts, consistent in sign, for both the vertical and

horizontal results on cumulative production (Equation 1) include Initial Reservoir Pressure,

Permeability Thickness (+), Fracture Half Length (+), Dimensionless Fracture Conductivity (+),

Difference (-), and i Days (i = 30, 60, 90, 180, 360, 720). As would be expected, cumulative

19 To carry out the tests, the paired bootstrap was used to estimate the variances of the two estimators being contrasted (see Cameron and Trivedi 2005). Instruments in addition to those specified in the system are used in order to carry out some the specification tests. However, we do not have sufficient valid instruments to carry out the 2nd and 3rd Hausman tests for the first equations in each system simultaneously. Therefore two tests are carried out, the first for the set of exogenous explanatory variables specified conditional on the time effects, the second for the set of time effects, conditional on the specified exogenous X variables.


21

production increases over time, but at a decreasing rate. In the case of vertical wells, the CnF

concentration is positive and significant. In the case of the horizontal wells, while the surfactant

concentration is negative and significant, the CnF intercept and interaction terms are positive and

significant. Thus, CnF has a statistically different impact on early period cumulative production

relative to traditional surfactants. In addition, as expected, the parameter estimates for all time

dummies are significant and positive. Similarly, there are variables in each of the systems for

equations 2 and 3 that are statistically significant and of the same sign across the two models.

However, the vertical versus the horizontal technology results diverge for some variables.

There are a number of cases in which a parameter estimate is significant for one technology and

not in the other (e.g., Initial Reservoir Pressure in Equations 2 and 3); or the signs of the

parameter estimates vary (e.g., Winter Fracture in Equations 3); and/or the magnitudes of the

parameter estimates are different (e.g., Initial Reservoir Pressure or Fracture Half Length in

Equations 1).

We next consider the estimated direct and indirect cumulative production impacts of the

variables specified in the models. Table 5 provides cumulative production elasticities with

respect to the continuous variables in the models. These elasticities include both direct and,

where applicable, indirect impacts of the variables on cumulative production. The probability

that the estimated elasticity is greater than zero is also provided in the table.


22

TABLE 5: Cumulative Production Elasticities* VERTICAL HORIZONTAL Variable Elasticity SE Prob>0 Elasticity SE Prob>0

Reservoir Characteristics Initial Reservoir Pressure 2.335 0.330 1.000 0.521 0.234 0.987 Permeability Thickness 0.564 0.027 1.000 0.440 0.029 1.000 Perforated Interval 0.075 0.068 0.863 n.a. n.a. n.a.

Completion Outcomes Final Fracture Half-length 0.509 0.088 1.000 0.354 0.046 1.000 Early Fracture Half-length 0.177 0.034 1.000 0.268 0.037 1.000 Dimensionless Fracture Conductivity 0.433 0.219 0.976 0.213 0.135 0.943

Completion Choices CnF 0.062 0.029 0.984 2.251 .486 1.000 Surfactant n.a. n.a. n.a. -0.027 0.016 0.055 Average Proppant per Stage 0.0017 0.029 0.523 -0.182 0.102 0.037 Proppant Concentration 0.11 0.057 0.972 0.213 0.135 0.943 Average Injection Rate 0.027 0.042 0.738 0.237 0.056 1.000 Average Treatment Pressure -0.314 0.124 0.006 n.a. n.a. n.a. Difference -0.221 0.035 0.000 -0.124 0.034 0.000 Stages n.a. n.a. n.a. 0.190 0.047 1.00

Production Ratio 0.541 0.218 0.994 -0.00088 0.0414 0.491 * The Delta method is used for standard error (SE) calculations. n.a. implies the variable is not applicable in the indicated model.

In the case of reservoir characteristics, the signs of the cumulative production elasticities

are consistent across the vertical and horizontal technologies. The reservoir characteristics that

determine final fracture half-length and dimensionless fracture conductivity indirectly impact

cumulative production, and also directly impact cumulative production if they are variables in

Equation 1. Initial reservoir pressure and permeability thickness are positively related to

cumulative production, as expected. These elasticities are precisely estimated. The magnitudes,

however, are substantially different - both initial reservoir pressure and permeability thickness

have relatively greater impacts on cumulative production with the vertical technology than with

the horizontal, all else equal. Perforated interval is also positively related to cumulative

production for the vertical wells, with the estimated probability of a positive impact of 86.3%.

Returns to the reservoir characteristics are decreasing except in the case of initial reservoir


23

pressure in vertical wells, where there is an estimated 2.335% increase in cumulative production

for every percentage increase in initial reservoir pressure.

Completion outcomes are also consistent in sign across the two well technologies - the

probability of a positive cumulative production elasticities is 94.3% or greater in all cases. The

cumulative production elasticities with respect to final fracture half-length and dimensionless

fracture conductivity indicate the percentage change in cumulative production, given a

percentage change in the respective variable, regardless of the source of the change in the

variable. For example, consider the cumulative production elasticity of 0.509 for final fracture

half-length for the vertical technology. Given a percentage increase in final fracture half-length,

this indicates that cumulative production increases 0.509%, irrespective of the source of the

percentage increase in the final fracture half-length. Note that this expected increase is only over

the relatively short time horizon, compared to the expected life of the well, represented in our

sample. In contrast, the elasticity is 0.354 for the horizontal technology, and can be interpreted

analogously. Both elasticities are large relative to their respective standard errors, so they are

precisely estimated.

Variation in the cumulative production elasticities is more pronounced with respect to the

completion choice variables. The completion choices that determine final fracture half-length

and dimensionless fracture conductivity will at least indirectly impact cumulative production.

They will also directly impact cumulative production if they are explanatory variables in the

cumulative production equation. For example, consider the central tendency of the impact of

proppant on cumulative production for the vertical versus horizontal technologies. For vertical

wells, a one percent increase in proppant implies an expected 0.0017% increase in cumulative

production, but the probability of this elasticity being positive is only 52.3%, so it is not very


24

precisely estimated. In contrast, for horizontal wells, a percentage increase in average proppant

per stage indicates an expected decrease in cumulative production of 0.182%, with probability of

96.3% that this elasticity is negative. As in all of these estimated elasticities, these are the central

tendencies for the ranges of the variables in our data. While we do not expect proppant in

horizontal wells to be counterproductive at all levels of proppant use, our results indicate that it

is highly likely to be negative, on average, for the levels of proppant used across wells in our

horizontal technology sample.20 This suggests that for the horizontal wells, the conventional

wisdom of larger completion jobs (i.e., more pounds of proppant) does not necessarily result in

higher cumulative production. Further research in determining optimal proppant use is

warranted.

Proppant concentration (pounds of proppant to gallons of fluid) impacts cumulative

production indirectly through Equation 3. The cumulative production elasticities with respect to

proppant concentration are positive for both vertical and horizontal technologies, with

probabilities of 97.2% and 94.3% respectively.

The elasticities for average injection rates are positive for both technologies, but this

probability for the vertical wells is only 73.8%. For the vertical wells we also include the

average treatment pressure, which has a negative elasticity with probability 99.4%. As discussed

previously, there was too little variation in the treatment pressure for the horizontal wells in our

sample, so it was not included in the econometric specification.

The cumulative production elasticities with respect to the differences between the

beginning of the completion job and the first day of production are negative for both

20 Fitzgerald (2015), discussed above, finds that proprietary additives are not significantly correlated with higher production by most firms.


25

technologies and very precisely estimated. That is, the longer it takes to complete the well, the

lower cumulative production. This impact is relatively larger for the vertical wells.

The cumulative production elasticity with respect to the number of completion stages for

the horizontal wells is positive, with a high probability. As explained above, the multi-stage

completion processes are not relevant for the vertical technology.

All of the vertical technology wells used CnF, which is highly likely to provide a positive

impact on cumulative production from these wells (with 98.4% probability). The point estimate

of the cumulative production elasticity for this impact is 0.062, i.e., a one percent increase in

CnF in a vertical well is expected to yield a 0.062% increase in cumulative production. Again,

note that this impact is only measured over the limited time horizon represented in the data so

actual cumulative production increases over the life of the well may be significantly larger (as is

the case with other impacts).

The cumulative production elasticity with respect to CnF in horizontal wells is 2.251,

which is calculated from the cumulative production elasticities with respect to surfactants and the

CnF interactions throughout the estimated equations in the horizontal system. This implies that

cumulative production is expected to increase by approximately 2.251% for every 1% increase in

CnF for horizontal wells, indicating that CnF use provides increasing returns in horizontal wells.

Given it is economic to use CnF at all in horizontal wells, this result implies that higher levels of

CnF would be economically efficient. The standard error for the 2.251% is approximately

0.486%, so this elasticity is precisely estimated. While these are rather dramatic results, a caveat

is in order, our results are sample specific, the horizontal sample is relatively small and contains

only three CnF wells, providing 20 of the 167 horizontal technology observations in the sample.


26

In addition to this marginal impact of CnF use, there is also a fixed shift in cumulative

production with the use of CnF in the horizontal technology, as discussed below.

As discussed above, traditional surfactants and CnF are substitutes in well completion.

There are a number of other surfactants used across the 147 horizontal technology observations

that use surfactants but their specifics are proprietary and we have no further information to

distinguish between their compositions, so they are treated together as a group. The cumulative

production elasticity with respect to these surfactants is estimated to be -0.027, with a probability

of 94.5% that the elasticity indeed falls in the negative range. That is, for the ranges of

traditional surfactants used in our horizontal sample, returns are likely to be negative. This

implies that they are highly likely to be counterproductive at the levels used in our sample.

Analogous caveats to those in our above discussion of proppant use apply here as well.

Finally in terms of estimated elasticities, as in the theoretical development above, not

only how the well is completed but also how it is produced will impact cumulative production.

The ratio (days of production to total days required for that production) is highly likely to have a

positive impact on vertical well cumulative production. The cumulative production elasticity

with respect to this ratio for vertical wells is 0.541, with a probability of 99.4% of a positive

elasticity. The likely impact for the horizontal wells is less certain. The analogous elasticity for

horizontal wells is much less precisely estimated to be -0.00088, with a probability of 50.9% of

being positive.

Table 6 provides semi-elasticities for completion choice variables that are binary and

have both direct and indirect impacts on cumulative production. The time effects are not

reproduced here, as they are already provided in the estimated cumulative production equations

in Tables 3 and 4. The winter fracture indicator is relevant for both the vertical and horizontal


27

technologies. Although not highly significant, the central tendency for the vertical technology is

that a winter fracture reduces cumulative production by approximately 0.64%, and cumulative

production for the horizontal technology decreases approximately 8.14%. However, given the

wide probability bounds around these point estimates, particularly for the vertical wells,

relatively little confidence can be placed in them.

TABLE 6: Discrete Effects Impacts on Cumulative Production* VERTICAL HORIZONTAL Variable Semi-elasticity SE Prob>0 Semi-elasticity SE Prob>0

Completion Choices Winter Fracture -0.0064 0.040 0.436 -0.0814 0.079 0.151 CnF Intercept n.a. n.a. n.a. 15.351 3.300 1.000 *Delta method standard errors. n.a. implies the variable is not applicable in the indicated model.

Next, consider the indicator variable for CnF, which is only applicable to the horizontal

technology model. The implication of using CnF versus a traditional surfactant, i.e., the fixed

impact of CnF in completion, all else equal, is on average an increase of 15.35 times the MCF in

cumulative production of a horizontal well completed with traditional surfactants. Again, this

seems a rather large impact and we must caution that our results are sample specific, the

horizontal sample is relatively small and contains only three CnF wells, comprising 11.976% of

the horizontal technology observations. Regardless, considering both this discrete result and the

marginal CnF impact above, i.e., the CnF elasticity presented in Table 5, the CnF wells in the

horizontal sample are significantly more productive than the wells that use traditional

surfactants.

So, for these data sets, using CnF (an environmentally benign additive and a substitute for

traditional surfactants) results in a positive impact on early period production, but the use of the

general category of surfactant (for the horizontal wells) has a negative marginal impact on early

period cumulative production. These results naturally lead to more questions, including: “What


28

is the optimal level of a surfactant or CnF?” and “Are there statistically significant differences

to production across the different additives and, if so, how do the more benign additives fare

relative to toxic additives?” Unfortunately, given that most additive ingredients are proprietary

information, the latter question may be one that goes unanswered. But it is clear from our results

for this sample, CnF enhances productivity relative to traditional surfactants, on average.

Naturally, analogous questions could be asked in the case of other production choices as well.

Natural gas production declines are to be expected in the production of pressure-driven

resources such as natural gas and oil, and are important in terms of the profitability of the well.

These declines naturally impact the quantity of gas produced each time period and the timing of

capital expenditures – i.e., re-fracturing and re-completing the well. Using our estimated models

above, we predict average daily well production by technology and days of production. For

example, the predicted average daily production for the 720-day horizontal technology wells is

compared to their actual production path in Figure 2.


29

Figure 2: Average daily production for two-year horizontal wells.

From these in-sample predictions, we also calculate the expected declines in average

daily well production, and compare them to our sample data. Both horizontal and vertical

technologies result in substantial declines over the first year of production. For horizontal

technology wells, the average decline for the 360 day wells in the sample is 35.44%, compared

to our in-sample prediction of 39.51%. For 720 day wells, the predicted decline in average daily

production is 62.07%, which is slightly below the average sample decline for these wells. Table

7 summarizes these results, and includes analogous results for vertical wells with 360 days of

production, which have relatively steep production declines over the first year of production.


30

Table 7. Predicted and sample average production declines (by technology and days of production)

Technology Horizontal Vertical Production days 360 day wells 720 day wells 360 day wells

Predicted decline (in-sample)

-39.51% -62.07% -55.1%

Sample decline -35.44% -64.67% -60.6%

The physical production implications of these results are that the manner in which a well

is completed matters, as do the characteristics of the well. While some factors impact vertical

and horizontal wells in a similar fashion, this is not true in all cases. The economic implications

of these results are that the marginal benefits gained from changing a completion or production

choice can be measured through the change in marginal production and the cumulative benefits

of that production can be weighed against the marginal costs of that action. Further, there is an

optimal completion choice for a given well or type of well, but that choice may differ

substantially between vertical and horizontal well technologies.

6.0 CONCLUSIONS AND FUTURE RESEARCH

Technological advancement has made economic production from shale gas plays viable.

However, the cumulative benefits and ultimate recovery from a shale gas well can be impacted

by the completion and production strategies utilized. We find a substantial difference in the

marginal impacts for a vertical and horizontal shale gas wells that could ultimately impact the

total recoverable reserves of the wells. Our findings include:

• Reservoir characteristics, as well as completion outcomes, impact horizontal and vertical wells in the same direction, but not necessarily at the same magnitude or probability.


31

• Completion choices are more variable in the impact on cumulative production and are not necessarily consistent in either sign, magnitude, or significance for the vertical versus horizontal technology.

• Different additives have different impacts. That is, the CnF wells are relatively

more productive than the non-CnF wells in the horizontal technology well set and cumulative production increases with the level of CnF in the vertical wells.

• The size of the completion job matters and “more” is not necessarily better, when

it comes to proppant.

These factors result in heterogeneous production functions for vertical and horizontal

wells and the recognition that a “one-size-fits-all” mentality can lead to a sub-optimal outcome.

Early production could be improved through a number of controls or control parameters, but the

value of this change has to be compared to the incremental costs of that change. Further, given

that discrete capital investments impact initial completion and early period production, there are

additional costs/benefits to understand over the longer term, hence longer-term analysis is

important - we consider at most the initial two years of the wells’ lives in this study. For

example, the marginal product (in terms of cumulative production) exhibits diminishing returns

to fracture length as well as for other choice variables in well completion and production. This

includes stages in the case of horizontal wells, or pounds of proppant used per stage. This is

consistent with the notion that a bigger may not always be better. Larger fractures and larger

completion jobs may not always be optimal, and, for the ranges in the wells analyzed here, larger

completion jobs may be counterproductive. However, the value and costs of obtaining the

product must be considered to determine the optimal fracture length, number of stages, amount

of proppant, etc.

Integrated analysis that simultaneously considers the economic and engineering aspects

of the problem can provide information that can be used by firms and investors to make better-

informed completion, production, and risk mitigation decisions. This work provides a first step


32

in integrating economic and engineering analysis and allows us to consider the impact of

alternative completion strategies on well productivity. On-going work will extend this to

consider a larger suite of wells and a wider array of factors. Included in this is the development

of multiple periodic capital investments and optimal completion and production over the life of a

well.


33

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