Learning Curve, Change in Industrial Environment, and Dynamics of Production Activities in Unconventional Energy Resources

sustainability

Article Learning Curve, Change in Industrial Environment, and Dynamics of Production Activities in Unconventional Energy Resources

Jong-Hyun Kim and Yong-Gil Lee *

Department of Energy Resources Engineering, Inha University, 100 Inharo, Nam-gu, Incheon 22212, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-32-860-7555

Received: 29 August 2018; Accepted: 14 September 2018; Published: 17 September 2018

Abstract: Since 2007, shale oil and gas production in the United States has become a significant portion of the global fossil fuel market. The main cause for the increase in production of shale oil and gas in the US is the adoption of new production technologies, namely, horizontal drilling and hydraulic fracturing. However, the production cost of shale oil and gas in the US is comparably higher than the production cost of conventional oil and gas. In 2014, the crude oil and natural gas price decreased significantly to approximately 40 dollars per barrel, and natural gas prices decreased to 3 dollars per million British thermal unit, and thus the productivity and financial conditions for the exploration and production of shale oil and natural gas for producers in the United States have worsened critically. Therefore, technological innovation has become one of the most interesting issues of the energy industry. The present study analyzes the trends in technological innovation having a relationship with production activities. This study calculates the learning rate of 30 companies from the petroleum exploration and production industry in the United States using an improved learning rate calculation formula that reflects the changes in the oil production ratio. Thus, more statistically confident calculation results and interpretations of strategic production activities with regard to changes in the industrial environment were achieved in this study.

Keywords: learning rate; shale gas; tight oil; unconventional; petroleum; exploration and producing company; upstream industry; technological development

1. Introduction The main focus of the global energy market is the upstream energy industry in the Unites States. Figure1 shows the crash in the Henry Hub Natural Gas and West Texas Intermediate (WTI) crude oil prices in 2014. The WIT crude oil price decreased from nearly 100 dollars per barrel in late 2014 and continued to decrease to under 40 dollars per barrel in early 2016. The Henry Hub Natural Gas price decreased significantly from nearly five dollars per million British thermal unit in 2014, whereas the natural gas price rebounded in early 2016. Since then, US crude oil prices settled at approximately 50 dollars per barrel between the middle of 2016 to the middle of 2017. Natural gas prices settled and fluctuated at approximately three dollars per million British thermal unit from the middle of 2016. After the crash in commodities prices, price trends have differed. Oil prices increased to 60 dollars per barrel in early 2017. However, natural gas prices have remained at approximately three dollars per million British thermal unit [1,2].

Sustainability 2018, 10, 3322; doi:10.3390/su10093322 www.mdpi.com/journal/sustainability Sustainability 2018, 10, 3322 2 of 11 Sustainability 2018, 10, x FOR PEER REVIEW 2 of 12

WTI crude oil & Henry Hub Natural Gas prices 160 14

140 12

120 10 100 8

per per barrel 80 6 60

Dollars 4 40

20 2

0 0 Britishperunit Dollarsmillion thermal 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

WTI spot HenryHub Natural gas spot

FigureFigure 1. Natural 1. Natural gas gas and and crude crude oil oil price price changeschanges from 2007 2007 to to 2018. 2018. WTI: WTI: West West Texas Texas Intermediate. Intermediate.

ExplorationExploration and andproduction production (E&P) (E& companiesP) companies in the in USthe have US have persistently persistently suffered suffered during during periods of lowperiods commodities of low commodities prices in theprices past in the three past years. three years. Particular Particular damage damage has has been been incurred incurred byby E&P companiesE&P companies producing producing shale gas andshale tight gas oil and or tight unconventional oil or unconventional petroleum, petroleum, which have which comparatively have comparatively higher production costs than conventional oil and gas. These players have recorded higher production costs than conventional oil and gas. These players have recorded huge losses huge losses while producing oil and gas, which have higher production costs than their commodity while producing oil and gas, which have higher production costs than their commodity prices. prices. From mid-2015 to late 2016, low crude oil and natural gas prices have brought costs down Fromamong mid-2015 several to late US 2016,shale E&Plow crude players oil [3 and]. This natural decrease gas in prices production have brought costs could costs indicate down the among severalbeginning US shale of E&Pan era players of cheap [3 and]. This unconventional decrease in productionoil and gas or costs could could be a temporary indicate the change beginning caused of an era ofby cheap the reduced and unconventional expenditures of oilE&P and companies. gas or could Curtis be [4 a] described temporary the change technological caused development by the reduced expendituresand changes of E&Pin engineering companies. performance Curtis [4] described factors related the technologicalto productivity. development Moreover, the and results changes in engineeringsuggest that performance the discovery of factors remaining related scientific to productivity. unknowns could Moreover, possibly theimprove results the suggestproductivity that the discoveryof unconventional of remaining petroleum scientiﬁc- unknownsproducing technologies. could possibly improve the productivity of unconventional petroleum-producingGao and You technologies.[5] emphasized and suggested improving economic efficiency and sustainability in shale gas production through technological developments throughout the US shale gas industry Gao and You [5] emphasized and suggested improving economic efﬁciency and sustainability from upstream to downstream. Middleton et al. [6] showed that technological developments improve in shale gas production through technological developments throughout the US shale gas industry production from single wells using fracturing technology. The authors also conducted simulations fromusing upstream data from to downstream. 20,000 wells from Middleton the Barnett et al. formation [6] showed from that 1994 technological to 2016. Wei et developments al. [7] analyzed improve US productionpatent datum from singleand suggest wellsed using that fracturingthe technological technology. development The authors of the shale also gas conducted industry simulationsstarted usingfrom data 1966 from, and 20,000 patent wells intensity from strengthened the Barnett from formation 2005 to from2016. Moreover, 1994 to 2016. they Weidescribed et al. the [7] most analyzed US patentinnovative datum technology and suggested in the shale that the industry technological, including development synthetic carbon of the oxide, shale fracture gas industry proppant, started fromhydraulic 1966, and fracturing patent intensity, and horizontal strengthened drilling. Additionally, from 2005 to they 2016. highlighted Moreover, emerging they described technology the in most innovativethe shale technology industry, such in theas model shale simulation industry,s includingfor more precise synthetic exploration, carbon fracturing oxide, fracture technology proppant, in hydraulicthe deep fracturing, pay-zone and, water horizontal treatment drilling., and environmental Additionally, protecti theyon highlighted technology. emerging technology in From the perspective of the economics of technology, production cost is a proxy for measuring the shale industry, such as model simulations for more precise exploration, fracturing technology in the magnitude of innovation, and innovation based on technical change reduces the production cost the deep pay-zone, water treatment, and environmental protection technology. as a result of technological development. A study on technological progress focusing on productivity Fromincrease thes was perspective conducted of by the Wright economics in 1936 of[8] technology,. The author analyze productiond the factors cost is inducing a proxy fora decrease measuring the magnitudein the production of innovation, costs of andthe airplane innovation industry. based This on technicalprior research change on reducestechnological the production development cost as a resultfoun ofd technologicala productivity development. gain that occurs A study according on technological to learned labor progress skills focusing based on on cumulative productivity increasesproduction. was conducted Technological by Wright development in 1936 analyzed [8]. The authoraccording analyzed to reputed the factors cumulative inducing production a decrease in theactivities production have costsbeen applied of the airplanein many ways. industry. This prior research on technological development found a productivity gain that occurs according to learned labor skills based on cumulative production. Technological development analyzed according to reputed cumulative production activities have been applied in many ways. Sustainability 2018, 10, 3322 3 of 11

The Boston Consulting Group (BCG) proposed an experience curve model with the same mathematic expression as a learning curve [9]. Furthermore, the BCG suggested that interpretation must be strategic using the experience curve method. In the competitive case, a market leader has the fastest growing productivity in terms of the experience curve and can try to compete to increase their market share by decreasing product prices. In the same manner, other competitive companies will react to competition on the expectations of changes in costs [10]. Cabral [11] provided an explanation of the relationship between price, learning, and cost. The authors showed that predatory pricing strategies to decrease prices and increase market share could decrease production as a result of quicker learning stimulated by competition. Ibenholt [12] calculated the learning rate for wind power generation and discussed policies for enhancing the learning effect based on competition. The authors also found that R&D funding is as important as technological development in pushing policies. In some cases, if R&D funding does not support enough technological development, production costs can increase. As a recommendation, it is important to promote and suggest a desirable target appropriately. Dutton and Thomas [13] examined four causal factors capable of affecting the learning rate, and divided the learning phenomenon into autonomous learning and induced learning. The first is technological development in the area of capital goods. In this case, cumulative investment is more important than cumulative production. The second is the Horndal-Plant-effect case. The Horndal-Plant effect refers to an increase in labor productivity through learning. The Horndal-Plant effect also indicates an improvement in the management of inventory, work scheduling, quality control, and wage incentives. The third is the local system characteristics, which are causal factors resulting in the dynamicity of learning in a specific industry. Finally, the fourth is the scale effect, which is a well-known causal factor of learning. The scale effect indicates technological development according to cumulative production, and it is a characteristic of an expanding market and the industrial production scale. Rapping [14] applied the learning curve concept to production function by adding the cumulative output as a factor in production function. Their results indicate that the cumulative output has a positive relationship with production amount even in models including labor and capital inputs. Kahouli-Bralmi [15] suggested an interpretation explaining the constant return to scale of the learning curve based on the Cobb–Douglas production function. There have been many applications of the learning curve method in analyzing the technological development of the energy industry. Samadi [16] examined and compared the learning rate of electricity generation technologies using a number of previous studies. Hong et al. [17] calculated the learning rate for photovoltaic power generation in Korea using a two-factor model based on cumulative production and cumulative investment and production costs. They showed that the magnitude of technological development based on investment is higher than technological development through production. Moreover, several studies have examined the learning rate of the upstream petroleum E&P industry. Fukui et al. [18] recently conducted a study regarding technological development and innovation of the upstream energy industry in the US based on production. He analyzed the learning curve of the shale E&P industry in the US using the well-head price and cumulative production. Kim and Lee [19] calculated the learning rate for shale players in the US using the production cost and drilling count. They compared their results from a traditional one-factor model of 3.08% to a learning rate of approximately 1.89% from 2008 to 2016. Due to the different learning rates, the research period was extended. Moreover, the authors found a meaningful decrease in production costs that was concentrated in the most recent three years. In the prior work, the meaningful period of decrease was just two years, from 2015 to 2016. Kim and Lee [19] found some improvements from the previous study and estimated the learning rate using the production cost and amount of crude oil and natural gas. Moreover, they categorized the learning curve into short and long periods. However, improvements still remain to be achieved through further research. The generally accepted rule of the petroleum E&P industry is to apply the thermal unit cost, barrels of oil equivalent (BOE) per dollar, or the equivalent Sustainability 2018, 10, 3322 4 of 11 of a thousand cubic feet of natural gas, integrating multiple petroleum products from a company. However, the actual thermal unit production cost differs between petroleum products. Thus, this study characterizes the effects of introducing the thermal unit cost of petroleum products into the learning curve method. By adapting an improved empirical method, this study demonstrates an improved statistical confidence level and draws conclusions that describe the causes of innovation from continuously repeated production activities. Moreover, this study provides a deep discussion on technological development, including changes in production activities and industrial environment.

2. Material and Methods This study is an extension of a previous study [19] and is an attempt to develop a learning curve model with higher statistical accuracy. Thus, this study mainly suggests an improved learning curve method appropriate for application to the upstream of the petroleum production industry. Additionally, this study contains a novel approach for technological progress.

2.1. Learning Curve Method The learning curve concept originates from a study by Wright [8]. Since then, many studies have been conducted to develop the learning curve method for analyzing the relationships between cumulative production and production cost [9–21]. This study suggests a new application of the learning curve with a presentation of previous models; the variables used within the models are summarized in Table1.

Table 1. Summary of variables used in the equations.

C X n Formula (1) Unit cost Produced quantity Decline rate C C x x L Formula (3) t 0 t 0 Initial unit Cumulative Initial Learning Unit cost cost production production parameter LR PR Formula (4) Learning rate Progress rate θ O Formula (6) Oil production ratio Coefﬁcient of oil production ratio

The authors characterized the relationship between a decrease in production cost and the production in the airplane industry, which is shown in Equation (1).

C = Xn, (1) where C is unit cost, X is produced quantity, and n is a factor reﬂecting variation of unit cost with produced quantity, termed the decline rate. n can be calculated from Equation (2).

n = log C/ log X, (2)

Alchian [20] suggested the learning curve using a statistical approach to introduce a representative form with several alternative progress functions. Through many follow-up studies [9–21], the mathematical expressions for the learning curve have become generalized [21] −L xt Ct = C0 × , (3) x0 LR = 1 − PR = 1 − 2−L, (4) Equations (3) and (4) are basic equations used to calculate the learning rate, where L is the learning parameter, Ct is the production cost, C0 is the initial production cost, xt is the cumulative Sustainability 2018, 10, 3322 5 of 11

production amount, and x0 is the initial production. In Equation (3), the traditional form of a learning curve indicates that the production cost can be deﬁned based on the initial production cost and increased cumulative production. In Equation (4), LR is the learning rate and PR is the progress ratio. The learning rate is deﬁned through a multiplier of the cumulative production variable, which means the rate of cost decreases at double the cumulative production amount. The mathematical expressions herein assume that the output from a product is not a mixture of multiple products sharing a particular production technology. However, the upstream petroleum industry produces two representative products, crude oil and natural gas, each having its own substitutability. Thus, the authors suggest a changed form of the learning rate that allows for a consideration of the substitutability of the products of the upstream petroleum industry.

−L xt O Ct = C0 × × θt , (5) x0

ln Ct = ln C0 − L ln Xt + O ln θt, (6)

In Equation (5), xt/x0 also indicates the cumulative production compared to initial production, and θ is the oil production ratio, i.e., the thermal weight of the oil production to the total amount of production. The multiplier O is a dedicated coefﬁcient for the oil production ratio in the natural log transformed equation. In this study, the BOE is adopted as an integrated unit of multiple products. Equation (6) is the natural log transformed expression of Equation (5), which is used in the statistical calculation. In Equation (6), Xt represents the cumulative production per initial production (xt/x0) in Equation (5). Y = B0 + B1 ln Xt + B2 ln θt + ε, (7)

Equation (7) is a statistical calculation of the learning rate. Here, B0 is the statistically estimated term for initial cost, B1 is the learning parameter, and B2 is the coefﬁcient of the oil production ratio affecting the changes in production cost. B2 is the multiplier for the oil production ratio in Equation (5). B2 reﬂects the effect of the oil production ratio change on production cost and takes a critical role in increasing statistical accuracy and can be obtained through the statistical calculations. Therefore, the learning rate from the proposed model, Equation (5), can be different from the traditional learning rate model, Equation (3), by adding the oil production ratio.

2.2. Data Collection Data were collected from an annual report on 30 companies publishing their individual annual ﬁnancial reports through the Security Exchange Commission of the United States [22]. In particular, they were selected as samples of independent E&P companies producing shale oil and tight gas in the United States. The production costs include the operating costs, production tax, depletion, depreciation, and amortization costs; general and administrative; and transportation and processing costs. Moreover, production data were also collected from an annual report, which include the amount of oil production in barrels of oil, the amount of gas production in cubic feet, and the entire production in barrels of oil equivalent.

3. Results and Discussion For an insightful discussion regarding the calculation results, descriptive interpretations of the market and industrial changes during the survey period are needed.

3.1. Calculation of Learning Rate In this study, several calculations were conducted to identify the learning rate for companies and the industry based on ﬁnancial and production data from the 30 companies. The speciﬁc calculation results based on Equations (1) and (3) are summarized in Table2. Sustainability 2018, 10, 3322 6 of 11

Table 2. Calculation of learning rate and comparison of models.

Traditional One-Factor Model, Equation (1) Learning B B R2 Adjusted R2 0 1 Rate Value 3.5115 −0.0452 3.086% 0.018 0.0148 less than Probability 0.0198 0.01977 (F-statistic) 0.000 Proposed Model,Equation (3) Learning B B B R2 Adjusted R2 0 1 2 Rate Value 3.8575 −0.0587 0.2480 3.989% 0.5626 0.5597 less than less than less than Probability less than 0.000 (F-statistic) 0.000 0.000 0.000 Differences between the Traditional One-Factor Model and Proposed Model,Equations (1)–(3) Learning B B R2 Adjusted R2 0 1 Rate Value 0.34604 −0.0135 0.903% 0.5445 0.5449 less than Probability −0.0198 0.0198 (F-statistic) 0.000

The learning rate for the traditional one-factor model is 3.086%, with an R-squared value of 0.0181 and an F-statistics probability of 0.0198. However, the R-squared value for the traditional one-factor model is very low. In Table2, the model proposed results in a learning rate of 3.989%, with an R-squared value of 0.5626 and an F-statistics probability very close to zero. The coefficient of the oil production ratio from the proposed model has a positive value, which is in contrast to the learning parameter. This implies that the oil production cost is higher than the natural gas production cost. Producers can change the product ratio between crude oil and natural gas, and changes in the product ratio could impact the production costs of the company. The proposed model results in a learning rate that is 0.903% higher than that of a traditional one-factor model. Critically, a large difference in statistical accuracy is shown. The R-squared value of the proposed model is 0.5445 higher than that of the traditional one-factor model. This impressive improvement may have been derived from the cost increase factor. As shown in Table3, the correlation value between production cost and cumulative production per initial production is −0.0642, and the correlation value between production cost and the oil production ratio is 0.3491. The absolute difference between these two correlation values is 0.2849. The probability coefficient for the production cost with the oil production ratio is 0.0198 smaller than the probability coefficient for the cumulative production per initial production with a full cost.

Table 3. Correlation values of production costs with cumulative production per initial production and oil production ratio.

Cumulative Production Per Oil Production Ratio, ln(θ) Initial Production, ln(xt/x0) Value −0.0642 0.3491 Probability 0.0198 less than 0.000

Moreover, as shown in Figure2, the correlation between the production cost and the oil production ratio is more concentrated and intensive. It also appears to be more accurate than the correlation between the production cost and the cumulative production per initial production. Sustainability 2018, 10, x FOR PEER REVIEW 7 of 12 Sustainability 2018,, 10,, 3322x FOR PEER REVIEW 77 ofof 1112

((aa)) ((bb)) Figure 2. Correlation between production costs with (a) cumulative production per initial production Figure 2.2. Correlation between production costs with ( a) cumulative production per initial production and (b) oil production ratio. and (b) oil production ratio.

Additionally,Additionally, the the residual residual distribution distribution ofof the the proposed proposed modelmodel appearsappears toto bebe moremore similarsimilar toto aa normalnormal distribution, distribution, as as shown shown in in F FiguresFiguresigures 3 33 and andand4 4.4..

FigureFigure 3 3.. ResidualResidual distribution distribution from from the the traditional traditional one one-factor-factor model. Figure 3. Residual distribution from the traditional one-factor model. As shown in Table4, the median and mean values of the residual distribution from the proposed model are closer to zero.

Table 4. Characteristics of the residual distributions from the two models.

Mean Median Standard Deviation Skewness Kurtosis Traditional one-factor model 1.06 × 10−15 0.0463 0.3976 −0.5450 3.0986 Model proposed in this study 3.72 × 10−16 −0.0181 0.2655 −0.1128 2.9018 Sustainability 2018, 10, 3322 8 of 11 Sustainability 2018, 10, x FOR PEER REVIEW 8 of 12

FigureFigure 4. 4Residual. Residual distribution distribution fromfrom the proposed proposed model. model.

3.2. DiscussionAs shown of Technological in Table 4, the Development, median and Changesmean values in Market of the residual Environment, distribution and Production from the Activitiesproposed for the 30 E&P Players model are closer to zero. To summarize the changes in the Henry Hub Natural Gas and WTI crude oil prices for the last 10 years, the commoditiesTable 4. pricesCharacteristics crashed of the in 2008residual and distr 2014.ibution Henrys from Hubthe two natural models. gas and WTI crude oil prices were decoupled in 2010 compared to the WTI crudeStandard oil price, which increased to nearly Mean Median Skewness Kurtosis 100 dollars per barrel, and high oil prices continued until late 2014.Deviation In contrast, the Henry Hub Natural Gas priceTraditional ﬂuctuated one-factor at approximately model 1.06E four- dollars15 0.046 per million3 British0.3976 thermal−0.545 unit0 from 20093.098 to6 2014. TheModel commodities proposed in price this study crash was3.72E induced-16 by− OPEC0.0181 predation,0.2655 i.e., predatorial−0.1128 action2.9018 with price declines to compete with rival companies [11]. This action is also termed a squeezing strategy [23]. BCG’s3.2. [Discussion10] experience of Technological curve model Development, includes Changes several in Market competitive Environment, applications and Production such asActivities predation. In somefor the cases 30 E&P in Players the US, the price change occurs in two phases. First, the price continues at the pre-existing level. Second, the price suddenly decreases. These two phenomena are the result of To summarize the changes in the Henry Hub Natural Gas and WTI crude oil prices for the last strategic10 years corporate, the commodities competition, prices which crashed results in 2008 in aand temporary 2014. Henry increase Hub natural in productivity gas and WTI to survivecrude oilthe competition.prices were Cabral decoupled [11] suggested in 2010 compared that the positiveto the WTI side crude of cost oil competitionprice, which isincreased that companies to nearly learn 100 to developdollars their per productivity barrel, and high when oil theyprices are continued under pressure until late from 2014. price In contrast, decreases. the Henry Hub Natural GasAs price shown fluctuated in Figure at5 ap, theproximately changes infour production dollars per costs million from British 30 companies thermal unit were from similar 2009 to to 2014. those in commoditiesThe commodities prices. However, price crash in was 2017, induced they by did OPEC not increasepredation but, i.e., rather predatorial decreased action comparedwith price to the previousdeclines to year. compete This with phenomenon rival companies was due [11 to]. This an increase action is in also operating termed a costs squeezing and production strategy [23 taxes]. accordingBCG’s to[10 an] experience increase incurve production. model include The Energys several Information competitiveAdministration applications such of as the predation. United States In pointedsome out case thats in an the increase US, the inprice production change occurs costs fromin two upstream phases. First, companies the price was continues based on at anthe increase pre- in costexist factorsing level. according Second, the to aprice greater suddenly need decreases. for production These two facilities, pheno tools,mena are and the equipment result of strategic [24]. In a predationcorporate case, competition, increases in productionwhich results cost in could a temporary cause company increase closure in productivity because some to companiessurvive the fail to successivelycompetition. increase Cabral [11] productivity suggested that [11]. the However, positive someside of upstream cost competition companies is that successfully companies learn achieve to develop their productivity when they are under pressure from price decreases. higher productivity based on technological development [4–7]. This has resulted in a change in As shown in Figure 5, the changes in production costs from 30 companies were similar to those productivity and oil production ratio in recent years. in commodities prices. However, in 2017, they did not increase but rather decreased compared to the previousWhile high year. oil This prices phenomenon were sustained, was due annual to an productionincrease in operating increased, costs and and the oilproduction production taxes ratio rapidlyaccording increased to an from increase 2011 in to production. 2014, as shown The Energy in Figure Information6. Administration of the United States pointedIn 2014, out oil that and an increase natural in gas production prices crashed costs from and upstream were recoupled. companies was From based 2015 on toan earlyincrease 2017, WTIin oil cost prices factors were according under to 60 a dollars greater perneed barrel, for production and the Henryfacilities, Hub tools, Natural and equipment Gas price [24] remained. In a at approximatelypredation case, three increases dollars in p perroduction British thermalcost could unit. cause While company the commodities closure because prices some were companies recoupled, annual production increased, whereas the oil production ratio slightly decreased, from 2014 to 2016. In 2017, the oil production ratio slightly increased, but it was still below the oil production ratio in Sustainability 2018, 10, x FOR PEER REVIEW 9 of 12

Sustainabilityfail to successively2018, 10, 3322 increase productivity [11]. However, some upstream companies successfully9 of 11 Sustainabilityachieve higher 2018, pr10,oductivity x FOR PEER basedREVIEW on technological development [4–7]. This has resulted in a change9 of 12 in productivity and oil production ratio in recent years. 2015. In addition, there may have been another reason for the increase in proportion of gas production. fail to successively increase productivity [11]. However, some upstream companies successfully Someachieve studies higher have pr shownoductivity that based the reason on technological for an increase development in global [4 gas–7]. dependencyThis has resulted on energy in a change use has Production cost beenin dueproductivity to environmental and oil production concerns ratio and in the recent production years. of shale oil and gas [25]. 45 40 Production cost 35 45 30 40 25 35 20 30 15 25 10 20 5 15 Dollars/ Barrelof oilequivalent 0 10 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 5

Dollars/ Barrelof oilequivalent Production cost GnA 0 2008 2009 DDnA2010 2011 2012 2013Operating2014 2015 2016 2017 Transportation and processing Production taxes Production cost GnA DDnA Operating Figure 5. Changes in production costs and detailed description of composites. Transportation and processing Production taxes While high oil prices were sustained, annual production increased, and the oil production ratio rapidly increasedFigureFigure from 5. 5Changes. 2011Changes to in2014, in production production as shown costscosts in Figureand detailed detailed 6. description description of ofcomposites. composites.

WhileThousand high oil barrels prices were sustained, annual production increased, and the oil production ratio rapidly increasedof oil equvalent from 2011Annual to 2014, production as shown inamount Figure & 6. oil production ratio 1,800,000 0.6 Thousand1,600,000 barrels 0.55 of oil equvalent Annual production amount & oil production ratio 1,400,000 0.5 1,800,000 0.6 1,200,000 1,600,000 0.45 1,000,000 0.55 1,400,000 0.4 800,000 0.5 1,200,000 0.35 600,000 0.45 1,000,000 0.3 400,000 0.4 800,000 0.25 200,000 0.35 600,000 0 0.2 0.3 400,000 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 200,000 0.25 Integrated thermal unit production Oil production ratio 0 0.2 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 FigureFigure 6. Changes 6. Changes in in annual annual petroleum petroleum productionproduction and oil oil production production ratio ratioss for for 30 30companies companies in the in the upstream petroleum industryIntegrated in thethermal Unites unit States production from 2008 to 2018.Oil production ratio upstream petroleum industry in the Unites States from 2008 to 2018. Recently,FigureIn 2014, 6 there. oChangesil and have natural in beenannual gas opposing petroleum prices crashed expectationsproduction and andwere and oil re productioncoupled. interpretations From ratios 2015for on 30 to thecompanies early issue 2017, of in decreasing theWTI oil productionpricesupstream were costs underpetroleum for shale 60 industry dollarsoil and in gasper the producers barrel, Unites States and [ 3 thefrom]. First, Henry2008 the to 2018. Hub decrease Natural in the Gas production price remained cost of atshale oil andapproximately gas will not three continue dollars becauseper British the thermal decrease unit. in While production the commodities costs depends prices onwere a cutrecoupled, in service In 2014, oil and natural gas prices crashed and were recoupled. From 2015 to early 2017, WTI oil costs,annual and thereproduction are almost increased, no technological whereas the oil developments production ratio to decreaseslightly decreased, production from costs. 2014 Second, to 2016. the pricesIn 2017, were the oil under production 60 dollars ratio perslightly barrel, increase and d, the but Henry it was Hub still below Natural the Gas oil production price remained ratio atin positive expectation on productivity gain means that a decrease in production costs will continue or a approximately three dollars per British thermal unit. While the commodities prices were recoupled, decreaseannual in production production increased, costs will whereas be sustained the oil inproduction the future ratio because slightly some decreased, technological from 2014 developments to 2016. willIn occur 2017, to the increase oil production efficiencies ratio [ 4slightly,6,7]. increased, but it was still below the oil production ratio in Moreover, some studies on learning rate have suggested that the increase in the unit production scale will have a simultaneous learning effect. The BCG [10] suggested that the change in scale occurs Sustainability 2018, 10, 3322 10 of 11 simultaneously with total experience, i.e., the change in scale affects the increase in efficiency and productivity. Ibenholt [12] argued that an increase in unit production scale is characteristic of successful learning. Moreover, the authors emphasized appropriate goals in developing learning technology. Recent studies have shown that technological development enhances profitability, such as design optimization, which suggests that the US upstream industry is finding appropriate technological development goals [5,26]. Curtis [3,4] showed the technological development of the shale oil and gas upstream industry by presenting engineering factors, including the estimate of ultimate recovery (EUR) and increases in single-well production from decline curve analysis. In other words, recent technological changes in the shale oil and gas upstream industry show that the increase in the production scale of a single well or rig results in an increase in productivity from the shale oil & gas upstream industry. Moreover, recent innovation activities of the US petroleum upstream industry have focused on productivity gains [7].

4. Conclusions The upstream petroleum industry in the United States has started to produce shale gas and tight oil at high production costs. In 2014, the commodities prices crashed, and technological development became one of the most interesting issues of the global energy market. To consider the technological development that took place after this crash in commodities prices, the learning rate for 30 upstream companies producing shale oil and tight gas in the US was calculated in this study. The results show that proposed model achieves a learning rate of 3.989%, whereas the learning rate from a traditional one-factor model is 3.086%. This difference is derived from the factors that are considered to increase production costs. From 2011 to 2014, the oil production ratio increased, as did the production costs. However, after 2015, the oil production ratio and production costs decreased despite an increase in annual production. This contrasting production trend compared to the production trend prior to the commodities price crash indicates that unconventional production companies in the United States increased their productivity by changing the portion of their shared petroleum production technologies and achieved substitutability. Additionally, this study suggests a new formula for calculating the learning rate. The actual production costs of products in certain industries can ﬂuctuate according to environmental changes in the market, as seen in the case of upstream industries in the United States. As a result, a high statistical accuracy was achieved in this study for calculating the learning rate by adding the cost-increasing factors. This study refers to some recent technological developments in the introduction and discussion. These technological developments are not coincidental, but a result of innovative activities such as investment. Even with improved statistical accuracy, this study has limitations; it does not cover innovative activities that could induce technological development. Therefore, further research is required to measure the effect of innovation activities on productivity gains or technological developments.

Author Contributions: Conceptualization, J.-H.K. and Y.-G.L.; Methodology, J.-H.K. and Y.-G.L.; Software, J.-H.K.; Validation, J.-H.K. and Y.-G.L.; Formal Analysis, J.-H.K. and Y.-G.L.; Investigation, J.-H.K. and Y.-G.L.; Resources, J.-H.K. and Y.-G.L.; Data Curation, J.-H.K.; Writing-Original Draft Preparation, J.-H.K.; Writing-Review & Editing, Y.-G.L.; Visualization, J.-H.K.; Supervision, Y.-G.L.; Project Administration, Y.-G.L.; Funding Acquisition, Y.-G.L. Funding: This study was funded by Inha University. Conﬂicts of Interest: The authors declare no conﬂict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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