Examining the Sustainability of the US Shale Oil Boom
Vladimir Bejan∗
March 1, 2018
Abstract
This paper proposes a model that allows to measure the otherwise unobservable contri- bution of improved shale extraction techniques on oil production in six unconventional oil regions. The ndings suggest that shale producers were able to increase production per rig for about 1.5 years after the collapse of oil prices in mid 2014. However, this phenomenon was short lived. In 2016, the production per rig declined dramatically, which puts into question the sustainability of the US shale boom in the absence of higher oil prices.
JEL Codes: C32, C51, Q49
1 Introduction
The shale boom, which involved the utilization of a combination of relatively new technologies to unlock huge supplies of unconventional oil resources, started transforming the energy landscape of the United States since 2007. These resources, known as shale and tight oil, are trapped in unconventional formations that make extraction very di cult because of their extremely low porosity and permeability. Prior to 2007, the US economy was in a precarious situation, partly because of its high dependency on foreign oil imports (Rapier,
∗ Department of Economics, Seattle University, Seattle, WA, 98122 (USA), Phone: (206) 296-5799, Fax: (206) 296-2486 (shared), email: [email protected].
1 2017). About 50% of the total US daily oil consumption in 2005 was covered by crude oil imports, corresponding to 10.1 million barrels of oil per day (bbl/d).1
High crude oil prices and low interest rates made it economically feasible for oil extraction companies to introduce new technologies, which substantially increased US oil production. These technologies involved a combination of extraction techniques such as hydraulic frac- turing (or fracking) and horizontal drilling. According to the Energy Information Adminis- tration (EIA), the US oil production rose from 5.2 million bbl/d in 2005 to 9.4 million bbl/d in 2015 (EIA, 2017f). Moreover, total US oil imports decreased from 12.5 million bbl/d in 2005 to 7.3 million bbl/d in 2015, corresponding to approximately 38% of the U.S. daily consumption (EIA, 2017a).
In 2016, the US consumed 7.2 billion barrels of petroleum products, representing an average of 19.69 million barrels per day, out of which 25% were imported (EIA, 2017d,c). However, this gure might be misleading since on December 2015, the 40-year old oil export ban imposed to the US was lifted, allowing US rms to export domestically extracted oil (Harder and Cook, 2015). The US mostly imports heavy, sour crude oil because this is what the Gulf of Mexico re neries are designed to process. Conversely, the unconventional oil produced in the US which is mostly of a light, sweet variety, tends to be more popular among foreign re ners. This partially has to do with the inability of domestic re neries to process this type of crude oil, which has led to a glut of oil in the US market. This surplus explains why West Texas Intermediate (WTI) prices, the US benchmark of light, sweet crude oil, are quoted at a discount compared to Brent, the global benchmark of oil prices.
Because of the shale oil production, the US became a major oil player in the global stage. In 2016, the US was the largest producer of petroleum and natural gas hydrocarbons in the world (EIA, 2017e), and this was possible because of the shale revolution. However, a question that emerges is whether this increase in oil production is a temporary or permanent scenario that will remain sustainable through time. This paper is intended to address this question.
***Place Figure1 about here***
The rst plot in gure1 shows the cumulative oil production from six shale regions listed in alphabetical order: Bakken, Eagle Ford, Haynesville, Marcellus, Niobrara and Permian. The
1The reason there is another b in the abbreviation of barrels per barrel has to do with the historical convention that the original 42 gallon barrels were blue in color (Raymond and Le er, 2005).
2 shale oil production increased from several hundred thousand bbl/d in 2007 to 5.5 million bbl/d in 2014. This illustrates the eect of the shale boom. Currently, about half of the oil produced in the US is from unconventional sources.
The middle plot in gure1 shows the total number of drilling rigs in the aforementioned regions. The EIA uses the number of drilling rigs in their model to make long run projections about future oil production. The Drilling Productivity Report (DPR) by EIA, also uses the number of drilling rigs as an indicator about oil and gas production in tight oil formations to make short run projections (EIA, 2017b). Moreover, crude oil prices in nancial markets are moved by drilling rig reports. This means that although the drilling rigs do not pump oil, they are used to obtain a reasonable estimate of oil production in each region. Visual examination of the top two plots in gure1 indicates that between 2012 and 2014 the number of rigs essentially stayed at while production was increasing. This suggests that US exploration and production (E&P) rms were able to extract more oil per rig, which is most likely attributed to the shale revolution.
The bottom plot in the same gure shows the oil price of Brent benchmark expressed in 2010 US dollars. In June 2014, the oil market started tumbling and the price of Brent fell to 25 in ation adjusted US dollars within two years. Even though the number of drilling rigs decreased since the second half of 2014, the total US production decline was substantially lower than expected, which points even further to the resilience of shale oil producers to survive in a low oil price environment.
The objective of this paper is to evaluate the sustainability of the US shale boom by examin- ing the changes of production per drilling rig through time. Here, a time varying parameter model is estimated to extrapolate a measure of the advancement in technology attributed to the fracking revolution. Because the US has experienced a low oil price environment for the past three years, if the shale boom is sustainable, then the extrapolated technologial series is expected to have a positive contribution to the production per drilling rig. The implication of these results is that under a sustainable oil production scenario, the US oil in- dustry should not be in uenced by the Organization of the Petroleum Exporting Countries' (OPEC) pricing policy.
The remainder of this paper is organized as follows. Section2 discusses the data used for this analysis. Section3 introduces the empirical model used to extrapolate the time varying portion of the drilling rig's productivity attributed to technological improvements. Section 4 discusses ndings and implications. Finally, section5 contains concluding remarks.
3 2 Data
In this paper, monthly total US oil production and number of drilling rigs from the following tight oil and gas formations: Bakken, Eagle Ford, Niobrara, Marcellus, Haynesville and Permian regions are used to estimate the contribution of the shale boom to the average production per drilling rig. The individual shale play data for total oil production and number of rigs are obtained from the US Energy Information Administration's DPR (EIA, 2017b), where each region is delineated along county lines and not by geological formations. Baker Hughes(2017) only includes those rigs in its weekly report, which represent drilling production wells. This means that wildcat rigs and service rigs are not included in the reported number. The monthly data covers the period from January 2007 to December 2016. Table 1: Summary Statistics Oil Production (1000 bbl/d) Bakken Eagle Ford Haynesville Marcellus Niobrara Permian min 131.83 47.72 43.10 8.70 112.95 834.05 max 1263.55 1710.89 62.95 45.00 497.93 2403.27 mean 645.76 677.41 53.51 24.29 255.70 1311.32 std.dev 401.68 606.17 4.98 12.59 135.14 469.33
Number of Rigs Bakken Eagle Ford Haynesville Marcellus Niobrara Permian min 24.00 30.00 16.00 24.00 16.00 92.00 max 218.00 279.00 244.00 141.00 127.00 565.00 mean 111.44 139.68 110.40 75.71 74.90 326.37 std.dev 64.78 90.73 75.44 32.06 30.34 135.63
Table1 presents the summary statistics of the data series used in this analysis. Comparing maximum production across formations it can be seen that the Permian region is by far the largest producer, followed by Eagle Ford and Bakken formations. Niobrara is in the distant fourth place, and Haynesville and Marcellus are marginal producers since they produce smaller amounts of oil compared to other regions.
***Place Figure2 about here***
Because we are dealing with time series data that wildly uctuates over time, it is more useful to examine the actual plots of the corresponding series. Figure2 plots total oil production
4 and rig count per shale region. In each region, the top plot shows oil production measured in thousand bbl/d, and the bottom plot shows the number of operating rigs over time. Focusing on the data for the Bakken region (top left plot, gure 2a), oil production peaked at the end of 2014 and declined in 2015 and 2016 with a slight rebound at the end of 2016. This decrease in production coincides with a decrease in the rig count. Starting in the second quarter of 2016, the rig count bottomed out and started an upward trajectory, yet the production was still decreasing.
Comparing production data among the reported shale plays, the Permian region is by far the largest oil producing region that appears to be growing. It currently produces about 2.4 million bbl/d. The second highest shale oil producing region is Eagle Ford which production peaked at 1.6 bbl/d at the beginning of 2015. The third largest producing region is Bakken. Similarly to Eagle Ford, its production peaked in 2014-2015 and produced slightly less than 1.2 million bbl/d at its peak. Marcellus is at a distant fourth place and produced less than 500 thousand bbl/d in 2014. The remaining two plays, Haynesville and Marcellus, are marginal oil producers that currently produce less than 40 thousand bbls/d each. In general, even though natural gas is produced when drilling for oil, some plays such as Haynesville and Marcellus are considered primarily as natural gas formations (Steinho et al., 2011).
Focusing on the four major oil producing plays (Bakken, Eagle Ford, Niobrara and Permian), one can observe that with the exception of Permian, the production peaked right around the time oil prices collapsed in 2014. Correspondingly, the rig count decreased as well around the same time because exploration and production (E&P) companies were forced to trim capital expenditure (CAPEX) as their revenues dried up signi cantly (Reuters, 2016a). For example, three major US shale oil producers Hess Corporation, Continental Resources and Noble Energy cut their CAPEX guidance for 2016 by 40-66% in order to survive the low oil price environment. Overall, North American producers cut their CAPEX budgets by 54 billion US dollars, which corresponds to a 40% decrease from the previous year, and in 2015 the budget was cut 29% (Reuters, 2016b).
Another interesting observation is that the rig count in these four plays neither stayed at nor decreased between 2012 and 2014, while at the same time, oil production increased. This provides evidence indicating that the shale revolution indeed took place because E&P rms were able to extract more oil by utilizing roughly the same number of rigs. However, as oil prices tumbled in the second half of 2014, this pattern broke and a decline in oil production that coincides with a decrease in the number of operating rigs was observed in the Bakken, Eagle Ford and Niobrara regions. The Permian region is the only region that
5 continued ramping up oil production despite low oil prices, and even here one can observe that a decrease in the rig count in 2015 slowed the production growth. Visual examination of the top subplot in gure 2f shows that there is a change in the slope of oil production that coincided with the oil price collapse. This evidence indicating that after oil prices collapsed in 2014, the shale oil production decreased in 3 out of 4 major shale regions, while the Permian basin kept increasing its production, puts into question that the shale revolution is mainly driven by improved extraction techniques. It is quite possible that in addition to the improved technology, it may also depend on the amount of proli c drilling spots available. This point will be revisited later in the paper.
3 Methodology
This paper examines the evolution of oil production per rig in several shale oil formations. The fact that the shale revolution allowed E&P to extract/recover more oil from the same number of wells indicates that this relationship is not constant over time. Figure2 pro- vides evidence indicating that, in the 2012-2014 period, while the number of drilling rigs either stayed at or decreased, oil production increased in the 4 largest shale regions until the oil price collapse. In the low oil price environment (post 2014), oil production declines are associated with a lower number of drilling rigs. In order to model such behavior stan- dard methods that assume some form of constant relationship applied to time series data no longer apply. One has to use a methods that capture the time varying nature of the relationship between variables. This is particularly important given the claims that the US fracking revolution continues today (Kemp, 2017). This is where time varying parameter (TVP) models that are mapped into a state-space form become useful. These models have been in existence for a long time and were mostly used in, but not limited to, the eld of Macroeconomics (Nelson and Plosser, 1982; Harvey, 1985).
Let yt, xt and pt represent oil production, number of drilling oil rigs and production per rig in any given month, respectively. Equation (1) shows that total oil production at any point in time can be decomposed as the product of the number of rigs, reported every month, and the average production per rig, which is not reported but can be easily calculated.
yt = xtpt (1)
Even though production per rig can be computed by dividing total production by the number
6 of rigs, the average rig's productivity does not allow to make any inferences about the technological change that might be occurring over time. The only information that can be obtained is whether the average production per rig increases or decreases. Because the fracking revolution, in theory, allows oil companies to extract more oil from the wells due to the improved extraction methods, it is reasonable to assume that the increased oil production is going to be re ected in an increased output per drilling rig.
This paper proposes to model rig's productivity as a random walk with drift, where the drift term is modeled as a random walk itself. The second part is very important. If the model assumed only a random walk with drift, this would imply that the average rig's productivity is stationary around a deterministic time trend,2 which would not be a realistic assumption. By allowing the drift term to be a random walk itself, the model will be able to capture the changes in the average well productivity. Therefore, one standard deviation of the drift term itself can be thought of as a shock to the growth rate of the average well's production, which allows to model the average rig's productivity that is attributed to the changes in extraction techniques (i.e., shale boom).
The motivation for modeling changes in the growth rate of production per rig takes its roots in the eld of Macroeconomics. This notion was particularly used to capture the technological change in US macroeconomic aggregates. However, since the productivity variable is not directly observable, it must be imputed using econometric techniques. The debate ensued as to how to model the growth of real macroeconomic variables (see Nelson and Plosser(1982); Clark(1987); Watson(1986), among others). The dominant point of view at that time was that the economy grows around a deterministic time trend and most of the uctuations in real GDP occur because of the cyclical changes in the economy (business cycles). However, Nelson and Plosser(1982) showed that macroeconomic variables are better characterized as nonstationary processes, which cannot be modeled using a deterministic time trend, but rather as a stochastic process. Clark(1987) proposed an unobserved component model that allowed to reconcile both points of view and showed that the variations in real GNP due to cyclical and stochastic trend uctuations are roughly proportionally the same. In order to capture the economic productivity slowdown in late 1970s, Clark(1987) modeled the stochastic trend of the economy as a random walk with drift where the drift term itself followed a random walk. The latter allowed to capture shocks that have a permanent eect on the economy.
2A necessary condition would be to set the variance of this process to zero.
7 Adopting the notation from Kim and Nelson(1999), the observation equation is given by:
" # h i pt yt = xt 0 (2) αt
Re-writing equation (1) in matrix form:
yt = Htβt (3)
0 where Ht = [xt 0] and βt = [pt αt] . Equation (2) states that oil production in any given month can be thought of as the product of operational oil rigs and the amount of oil each rig
produces. This equation represents the time-varying parameter model where Ht is the data
matrix and βt is the unobserved state process which contains two variables pt, the average
rig's productivity, and αt, changes in the average rig productivity that are attributed to constantly improved extraction techniques.
The transition equation is modeled such that the unobserved state vector βt follows the following process:
p (4) pt = αt−1 + pt−1 + vt α (5) αt = αt−1 + vt
which can be re-written in matrix from as:
" # " #" # " # p 1 1 p vp t = t−1 + t (6) α αt 0 1 αt−1 vt
Equation (6) models the average rig productivity (pt) as a random walk with drift where the drift itself is a random walk process. The last part is particularly important because it
allows to capture the average rig's productivity changes over time. Therefore, αt captures the technological change that occurs in the US shale plays. As the shale extraction technology
advances, a typical rig should be able to extract more oil. Innovations in the level of pt are given by p, while innovations in its growth rate are given by α. The variance of α vt vt vt determines the slope of the rig's productivity.
8 Simplifying the notation for equation (6):
βt = F βt−1 + υt, υt ∼ N (0,Q) (7) " # " # " # 1 1 vp σ2 0 where F = , v = t and Q = v v0 = p is the covariance matrix of the t α t t 2 0 1 vt 0 σα unobserved state vector βt. Here, it is assumed that shocks to the growth rates of well's productivity and the productivity shocks themselves are uncorrelated, so the covariance is set to zero.3
The estimates of the state variables pt and αt depend on the two hyper-parameters in this model 2 2 . These hyper-parameters are estimated by maximizing the constrained max- σp, σα imum likelihood function4:
T T 1 X 1 X 0 lnL = − ln 2πf − η f −1 η 2 t|t−1 2 t|t−1 t|t−1 t|t−1 t=1 t=1
where ηt|t−1 is the prediction error of yt given the information set (t − 1) and ft|t−1 is the conditional variance of the prediction error. The state-space model (equations (2) and (7)) are estimated using a Kalman lter. Depending on the type of time series data (stationary or non-stationary) the starting values for the state vector and its covariance in the Kalman lter initialization do matter. If the data were stationary, one could use the unconditional mean and the covariance as initial values. In this particular case, most of the shale basin data are non-stationary, and the unconditional means and covariances do not exists. As a result, arbitrary values are assigned as the initial values of the state vector, and to re ect the uncertainty about this guess, the diagonal elements of the corresponding covariance matrix are set to a very large value. In addition, the rst 60 observations are used as a burn-in sample to calibrate the model (Harvey, 1993; Kim and Nelson, 1999).
From the Kalman lter procedure, the unobserved state vector series pt and αt are recovered. Because of the nature of the Kalman lter, at each time t, one is able to estimate the unobserved state vector βt that incorporates all the past information up until time t. In order to obtain more accurate estimates that utilize the entire available information set, one
3The estimation of the unrestricted model with cov p α , shows that the covariance is statistically (vt , vt ) 6= 0 insigni cant for all shale plays. 4The optimization routine is carried out using the Broyden, Fletcher, Goldfard and Shanno (BFGS) algorithm. All computations were carried out in R (R Core Team, 2017). Data and code are available upon request.
9 can save the smoothed estimates by applying a Kalman smoother which involves recursively iterating the following equations backwards for t = T − 1,T − 2,..., 1:
0 −1 βt|T = βt|t + Pt|tF Pt+1|t βt+1|T − F βt|t 0 (8) 0 −1 h −1 i 0 Pt|T = Pt|t + Pt|tF Pt+1|t Pt+1|T − Pt+1|t Pt+1|t FPt|t
where βt|t is the estimation of βt conditional on the information set up to time t. Similarly,
Pt|t is the covariance matrix of βt conditional on the information set up to time t. The initial values for smoothing, βT |T and PT |T , are obtained from the last iteration of the Kalman lter (Kim and Nelson, 1999).
4 Results
The estimation of the system of equations (3) and (7) using the Kalman lter procedure described in the previous section allows to obtain the model's hyperparameters, correspond- ing standard errors, and the state vector series pt and αt. Table2 shows the value of the estimated hyperparameters and the corresponding standard errors. Recall that 2 and 2 σp σα
are the variances of the unobserved state vector pt and αt, respectively. The rst, represents the shock to the level of the average productivity per rig, while the latter is the shock to the growth rate of the average rig's productivity, assumed to be caused by technological changes.
Table 2: Estimated hyperparameters
2 2 σp σα Bakken 0.9149 1.6017 (0.3891) (0.2146) Eagle Ford 2.0337 1.0809 (0.1817) (0.4706) Haynesville 0.0000 0.2790 (909.872) (0.0864) Marcellus 0.0001 0.1427 (274.5569) (0.0864) Niobrara 1.1478 0.7809 (0.1415) (0.2511) Permian 0.0099 0.5228 (0.5885) (0.0864) *Standard errors are in parentheses.
10 The Permian basin is the most talked about on national news and accounts for the bulk of tight oil production in the US, partly because it is a relatively new and proli c play. In this basin, the variation in oil production is mostly driven by the technological change parameter - this parameter is statically signi cant, while the other is not. This implies that in this basin, the shock to the growth rate of rig's productivity is the one that matters, and not the productivity of the rig itself. This shock may represent continued improvements in extraction techniques, or drilling wells that are closer to each other in order to reduce the down time of the drilling rig when it is moved to a new location. However, the variation in oil production is not necessarily caused by the design of the drilling rig itself. Haynesville is similar to Permian in terms of the shocks that explain variations in oil production because it is the innovation in the growth rate that matters the most, even though it produces a small fraction of Permian oil production.
For Bakken, another major tight oil formation that was popular before the collapse of oil prices and is slowly recovering its dominance in shale oil extraction, it is the combination of shocks that explains the variation in oil production, where most of this variation is explained by the growth rate of productivity.5 The Eagle Ford and Niobrara regions are similar to Bakken in the sense that both sources of variation are important. However, in their case, the shocks to the level of productivity are 1.5-2 times as important as the shocks to the growth rate in productivity.
***Place Figure3 about here***
In order to further assess the importance of each of the sources of variation, it is imperative to evaluate the model's performance and check how it ts the data before doing any inferences. For each region, gure3 plots the actual production per rig (computed by dividing the total production by the number of rigs) and the smoothed pt series that is treated as an unobserved state variable from the Kalman lter procedure. The points represent actual data reported by the EIA, while the solid line is the pt series estimated by the model. In each region, both actual and extrapolated series coincide, thus indicating that the smoothed estimates are an excellent approximation of the production per rig series for each of the shale regions.
***Place Figure4 about here***
5The sum of variances is 2.5166, and 64% of this variance is attributed to the variance of the growth rate.
11 Next, the contribution that the technological change makes on the productivity of an average
rig is examined. Figure4 plots the αt series which is the measure of the rig's productivity attributed to technological changes for each basin. The vertical axis measures how much of an average rig's production, expressed in thousand bbl/d, is attributed to technological improvements. The scale shows that, on average, the technological improvement contributes up to 10% to the total production per rig.6 Oil prices collapsed in 2014 and have stayed in this depressed price environment ever since, yet the ramp up in production per rig coincided with the collapse of oil prices. For all basins, the technological parameter shows steady increases that start in mid-2014 and peak at the beginning 2016. This evidence supports the notion that shale producers can be pro table in a depressed oil price environment. However, for Permian basin, the peak of technological contribution occurred in 2009, right after the nancial crisis caused by the collapse of the housing market in the US.
Examining gure 4a one can observe that in the middle of 2014 the technological change parameter started to decline until the beginning of 2015. After reaching its peak at the beginning of 2016, the series declined sharply in the rst quarter and stayed negative for the remainder of 2016. When the series αt is positive, it suggests that there is a positive contri- bution to the amount of oil pumped from the ground that is attributed to the technological improvement. However, the same series being negative suggests the average oil production per rig is reduced from the baseline scenario.
A similar behavior can be observed in the remaining regions with meaningful oil production. Even though the same claim applies to the Haynesville and Marcellus regions, these regions do not produce nearle as much oil compared with other basins in the US. That is, as oil prices collapsed in the second half of 2014, these regions were able to extract higher amount of oil per rig compared to the times of high oil price environment. By looking at this behavior, it was reasonable to assume that shale oil is a game changer and is here to stay. After all, there were claims by E&P rms' CEOs that shale producers can be pro table even at record low oil prices (Gurdus, 2017). However, results from this analysis show that this scenario may not be sustainable. As evidenced from the plots in gures3 and4, in the beginning of 2016, oil production per rig signi cantly decreased. The question lingers on how is it possible to reconcile such behavior? If one compares gures 2f, 3f and 4f it is clear that while the production in the Permian region has increased, the average rig's productivity has decreased. A possible explanation for this result is that oil producers are drilling more wells, even though each well's productivity has decreased compared to the productivity of wells
6 Compare the maximum value for technological change series αt and the average production per rig for the corresponding region in gure3.
12 drilled in 2014 and 2015. A similar case is observed in the remaining regions: Bakken, Eagle Ford and Niobrara.
This empirical analysis suggests that there was a brief period in 2014 - 2015 during which shale oil producers were able to extract more oil per existing well and that phenomenon was attributed to the shale revolution. However, this pattern broke in 2016 because the average well's productivity decreased substantially from the peak levels. One plausible explanation for this result is that E&P rms drilled the most proli c spots at the beginning of the low oil price environment and eventually ran out of the best drilling spots in 2016. Perhaps, this is why the average rig productivity declined fast as evidenced by the behavior of the technological series αt. This leads to the following conclusion, the shale revolution is not only driven by improved extraction methods such as hydraulic fracturing and longer horizontal laterals, but also by proli c drilling locations. Given that shale wells deplete at hyperbolic decline curves (Wachtmeister et al., 2017), it is imperative that shale oil producers nd new places to drill. Out of six shale regions in the US, only Permian was able to achieve this status - drill more spots in order to increase production even though the productivity per well decreased. This can be explained by the fact that Permian is one of the most oil rich formations in the world (Conca, 2017) that is being targeted by E&P rms, while the rest of the regions may not have the same amount of proli c spots.
4.1 Model Diagnostics
One of the advantages of the model proposed in this paper is its exibility to encompass other models. As mentioned earlier, I modeled the drift term of the transition equation as a random walk process. In this section, I test the performance of the proposed model against an alternative speci cation where the transition equation is modeled as random walk with drift. To obtain the alternative speci cation, I set the variance of αt in equation (7) to zero. This restriction is equivalent to estimating the following state-space model:
yt = xtpt (9) p p 2 (10) pt = α + pt−1 + vt , vt ∼ 0, σp
Since I imposed one restriction on the original model to obtain an alternative speci cation of the model given by equations (9)-(10), I can perform a likelihood ratio test for each region where the test statistic is distributed as a χ2 with one degree of freedom. Table3
13 shows the test results for each region. The likelihood ratio test con rms that the model proposed in this paper is preferred to the alternative speci cation for the top four tight oil producing regions: Bakken, Eagle Ford, Niobrara and Permian. The same conclusion cannot be reached for Haynesville and Marcellus regions. Given the last two regions produce mostly natural gas, it is clear that the proposed model is preferred to the alternative speci cation for oil producing regions.
Table 3: Likelihood Ratio Test
Log-Likelihood (L) Unrestricted Model Restricted Model ∆ (L) p-value∗ Bakken 452.0479 467.0533 15.0054 0.0000 Eagle Ford 494.7574 498.2596 3.5022 0.0008 Haynesville 257.5983 251.1805 -6.4178 1.0000 Marcellus 244.9667 232.1403 -12.8263 1.0000 Niobrara 398.5961 405.6713 7.0752 0.0002 Permian 445.2254 474.7709 29.5455 0.0000 *p-value is obtained for the test statistic of 2 × ∆ (L). The test statistics is a χ2 distribution with 1 degree of freedom. Unrestricted model: equations (1),(4),(5). Restricted model: equations (9),(10).
5 Conclusion
This paper estimates a time varying parameter model that allows to model the behavior of the average drilling rig's oil productivity in such a way that it captures the technological changes that are attributed to shale revolution. The examination of the extrapolated, otherwise unobservable productivity series suggests that the shale revolution and the continued success of the oil extraction boom from tight oil formations in the US depends not only on the improved recovery methods, but may be also a function of the number of proli c drilling spots. Given the extremely high depletion rates of unconventional oil wells compared to the conventional ones, it is imperative that E&P rms nd premier drilling spots, otherwise the shale boom may come to an end.
The main implication is that the US shale is not as immune to low oil prices as generally thought. It is widely believed that the US is a swing oil producer, and as such, immune to OPEC's oil pricing policies. The large increase in oil production experienced by the US, has giving it the power to cause downward pressure on global oil prices. However, the ndings in this paper suggest that although E&P rms' production does depend on the technology
14 available to them, it also depends on the amount of proli c drilling spots. The latter may be the most important factor, especially when oil prices are low. Consequently, the viability of shale oil may depend on higher oil prices which OPEC is currently trying to achieve.
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18 Figure 1: Shale Oil Production, Number of Rigs and Real Price of Oil
5000
4000
3000
2000 Production (1000 bbl/day)
1250
1000
750 Drilling Rigs 500
250
125
100
75
50 Oil Price (2010 USD)
25 2008 2010 2012 2014 2016
19 Figure 2: Oil Production and Number of Rigs per Region
1250
1500
1000
1000 750
500 500 Oil Production (1000 bbl/day) Oil Production (1000 bbl/day)
250
0 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016
200
200 150 Rigs Rigs
100
100
50
2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 (a) Bakken (b) Eagle Ford
60 40
55 30
50 20 Oil Production (1000 bbl/day) Oil Production (1000 bbl/day)
45 10
2008 2010 2012 2014 2016 2008 2010 2012 2014 2016
250
200
100 150 Rigs Rigs
100
50 50
2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 (c) Haynesville (d) Marcellus
500 2400
400 2000
300 1600
200 1200 Oil Production (1000 bbl/day) Oil Production (1000 bbl/day)
100 800 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016
125
500
100
400
75
Rigs Rigs 300
50 200
25 100
2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 (e) Niobrara (f) Permian
20 Figure 3: Production per Rig (Model vs. Actual Data)
● 40 ● ● ●
40 ● ● ● ●
● ● ● ● ● ● 30 ●● ●
30 ● ●● ●● ● ● ● ● ● ● ● 20 ● ● ● ● ● ●● 20 ●● ● ● ● ●● ● ● ●● ●● ● ● ● Production per rig Production per rig ● ● ● ● 10 ● 10 ● ● ● ●●● ● ●●●● ● ● ●●●● ●●● ●●●●●●● ●●●●●● ●● ●●●●●●●●● ●●●● ● ● ●●●● ●●●● ● ● ●●●●●● ●●●● ●● ●● ●●●●●● ●●●●●● ●● ● ●●●●● ●●●●●●●● ●●●●●●●●● ●●●●●●● ●● ●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●● 0 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 (a) Bakken (b) Eagle Ford
3 ● ●●● 1.6 ●● ● ● ● ●
● ● ●● ● ● 1.2 ● 2 ● ● ● ● ● ●● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● 0.8 ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ●●●● ●●●● ●● ●● ●● ● ●● ● Production per rig Production per rig ● ● ● ●●● ● ● 1 ● ●● ● ● ●● ●● ● ● ● ●● ● ●● ● ●●●● ●● ● ● ● ● 0.4 ● ● ● ●● ● ●● ● ● ● ●● ● ●● ●● ● ● ●● ●● ● ●● ● ● ●●● ●● ●●●● ●●● ● ● ● ● ●● ● ●● ●● ●●●●● ●●●●●● ● ●● ●●●●●● ●● ● ●● ●●●● ●●● ● ●● ●●● ●●●●●●●●● ● ●● ●● ●●● ●● ●●●●●●● ●●●●●●●●●● ●● ●●●●●●●●●●●
2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 (c) Haynesville (d) Marcellus
15 ● ● ● ● ● ●● ● ● ● ● ● 20 ● ● ● ● ●● 10 ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● 10 ●● ● ● ●
Production per rig ● ● Production per rig ● ● ● ● ● 5 ● ● ● ●● ● ● ● ● ● ●● ●● ●●●●● ●● ●● ● ● ● ● ●● ●● ●● ●●●●● ●●●●● ●● ● ●● ●●●●●●●●●●●●●●● ● ● ● ●● ●●●● ●●●●● ●● ● ●●●● ● ●●●●●● ●●● ●● ●● ●●●●●●● ● ●●●●● ●●●● ● ●●●●●●●●●●●●●●●●● ● ●●●●● ● ●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●● 0 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 (e) Niobrara (f) Permian
21 Figure 4: Production per Rig Attributed to Technological Advancements per Region
3
4 2
2 1
0 0 Technology Technology
−1
−2 −2
−3 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 (a) Bakken (b) Eagle Ford
0.75 0.25
0.50
0.00 0.25
Technology 0.00 Technology −0.25
−0.25
2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 (c) Haynesville (d) Marcellus
2
2
1
1
0
Technology Technology 0
−1 −1
−2 2008 2010 2012 2014 2016 2008 2010 2012 2014 2016 (e) Niobrara (f) Permian
22