We calculated the EROI of shale gas production by estimating both the energy produced from the average shale well from the actual shale gas production statistics reported by the Pennsylvania Department of Environmental Protection (PA DNR), and the energy consumed to produce that energy. The energy inputs used to produce the shale gas were derived from values reported in a number of different LCA analyses, including. (Burnham et al. 2011; Clark et al. 2011, 2013; Jiang et al. 2011; Stokes and Horvath 2006). For this study, emphasis is placed on the model developments of Jiang et al. (2011) and Clark et al. (2011).

Jiang et al. (2011) performed a greenhouse gas LCA on gas extraction process in the Marcellus Shale using the Carnegie-Mellon economic input–output (EIO) framework (Carnegie-Mellon 2010). Clark et al. (2011) performed an LCA on the same process but used the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model produced by Argonne National Laboratory (Wang 2008). While each approach offers a unique perspective to the identification of boundaries within the production process and is specifically used for evaluating GHG emissions, the fundamental inputs required in these GHG-LCAs are the same as those needed in our research.

The approaches diverge in the methodology of quantifying the inputs within what are called “process stages.” The process-based LCA approach, which is used by Clark et al. (2011), places a boundary around each process stage and then itemized the energy input and outputs within the boundary of the activity. For example, in the well drilling stage the energy content of the materials required for the well casing, drilling mud, and the diesel fuel used for drilling and transportation are included as energetic inputs. Jiang et al. (2011), on the other hand, used an EIO approach which uses financial cost data for specific processes and multiplies them by energy intensity factors, resulting in energy units. The energy intensity factors are generated by an input–output table with hundreds of economic sub-sectors. The advantage for the process-based approach is that all inputs and outputs within the boundary are accounted for, and these are generally the direct energy inputs and outputs of a process. The disadvantage is that the indirect inputs and outputs fall generally outside the boundary of analysis and are therefore ignored. The advantages and disadvantages of the EIO approach are almost opposite; i.e., the EIO approach is coarser and may not capture all direct and indirect energy inputs and outputs, but the indirect energy in a process stage is accounted for in the I-O framework used to generate the energy intensity ratios.

The hybrid LCA-EIO approach used here merges the best parts of both frameworks; i.e., the process method is used to assess all the direct energy inputs and outputs, while the EIO framework is used to assess upstream and downstream indirect costs.

Boundaries of Analysis

Using a modeled schematic of the pre-production and post-production processes adapted from Jiang et al. (2011) and Clark et al. (2011), our model (a hybrid LCA approach) is, at its core, a cost–benefit analysis at the energy unit level (Fig. 1).

Fig. 1 Analysis process stage boundaries Full size image

Well Production Data

The analysis of well production data includes 5119 active wells in Pennsylvania located throughout the Marcellus Shale play, and EUR values were estimated through decline curve analysis. The PA DNR collects well data in 6-month production periods, reporting average daily production within each period. The time horizon of the analysis is from January 2010 to July 2014 and includes only those active wells with an initial production (IP) period (first 6 months) within the designated time frame. The mean EUR of the wells included in the study is 3.16 billion cubic feet (Bcf), nearly 80 % of the wells analyzed are within ±1 standard deviation from the mean, 9 % of the wells were calculated as having an EUR of less than one standard deviation from the mean value, and 12 % of the wells studied reported production greater than one standard deviation (Table 1).

Table 1 Estimated ultimate recoverable (EUR) of analyzed Marcellus wells Full size table

Decline Curve Analysis: Estimated Ultimate Recoverable Gas per Well

The average EUR value is extrapolated from the average production values over a 4-year period (eight 6-month periods) and extended to a 30-year time horizon. Although the EUR is calculated based on the 30-year production well life, it is interesting to note that the average well included in the analysis produced 97 % of its EUR within the first 7 years of production. The available production values are fit to an exponential curve, where resulting average EUR is 3.16 Bcf (Fig. 2). The exponential estimate in the decline curve equation is derived from the available data and overestimates production values, as subsequent production periods are expected to decline more rapidly. In the first production period (January 2010 through June 2010), 821 wells began producing natural gas at an average of 1855 Mcf/Day. In the second producing period (July 2010 through December 2010), 430 newly active wells began producing at an average IP of 3480.66 Mcf/Day. Through advancements in technology and industry learning, the average IP levels from period one to period eight increased 245 % (from 1855 to 6392.17 Mcf/Day). Looking at year-over-year average IP change also offers clues about the resource base. For example, the greatest increase in average IP occurred from period one (January–June 2010) to period two (July–December 2010; an 88 % increase in average IP values), while the average increase from period seven (January–June 2013) to period eight (July–December 2013) is 19 %, indicating diminishing marginal returns (Fig. 3).

Fig. 2 Exponential decline curve for average well Full size image

Fig. 3 Average initial production by period Full size image

Energy Costs per Process Stage

Well Pad Preparation

The well pad preparation inputs were provided by Jiang et al. (2011); the CMU model provides energy content equivalents based on per unit costs in 2002 USD (CMU GDI, 2010). The total average cost for well pad preparation is estimated at $3.2 M (Jiang et al. 2011), and the CMU EIO model, utilizing an energy intensity ratio of 8.26 TJ/$1 M, yields an energy input of 26.33 TJ. Far and away, the most energy-intensive process during well pad preparation is construction of the slurry trench, accounting for 75 % of the total energy cost. The slurry trench is a waste site remediation requirement for the containment of subsurface pollutants and fracking flow-back water during well drilling and completion, and produced water from initial natural gas production (Table 2).

Table 2 Materials used in well pad preparation Full size table

Well Drilling

The direct materials required for well drilling are based on per well estimates provided by Clark et al. (2011). Energy content for the steel and cement used in the well casings was obtained from the literature within the respective industries. According to Stubbles (2000), the energy content per metric ton of steel is 17.29 GJ/MT, while Choate (2003) estimated the energy content of cement and gilsonite at 5.97 GJ/MT. Admittedly, as the processes involved in manufacturing both cement and gilsonite vary widely across the industry, it is important to note that cement and gilsonite contribute only minimally to the total direct material cost involved in producing the well casings, at 11.81 and 0.41 %, respectively. Diesel fuel consumption (Jiang et al. 2011) includes both average water transportation and average drilling estimates. Steel production and diesel fuel consumption represent the bulk of the direct material and energy costs associated with well drilling, at 20.71 and 66.50 %. The average cost per component of the drilling mud was estimated by Jiang et al. (2011) at $105/MT and equated to a total energy drilling mud cost of 78.76 GJ, which contributes 0.65 % to the remaining total direct drilling cost (Table 3). Jiang et al. (2011) estimate that the average total drilling cost per well within Marcellus is $2.2 M, at an intensity ratio of 11.40 TJ/$1 M, and is equivalent to 25.16 TJ of total energy costs, which can be further segmented into 12.15 TJ of total direct energy costs itemized in table five. The remaining 13.01 TJ (25.16 TJ − 12.15 TJ) is allocated to the indirect energy costs associated with well drilling.

Table 3 Materials used in well drilling Full size table

Hydraulic Fracturing

The hydraulic fracturing process stage includes two components: the energy costs associated with the management of fracturing fluid and the production of additives within the fracturing fluid. The average water requirement per well per fracturing job within the Marcellus Shale is 4.65 M gallons. The diesel fuel consumed during the fracturing process is estimated at 4444 gallons per well (Clark et al. 2011), converted to an energy equivalent of 650.29 GJ. The average electricity consumption per fracturing job is 659 kWh per well, equivalent to 2.37 GJ. The total energy costs associated with the management of hydraulic fracturing fluid per well per fracturing job are 652.66 GJ (Table 4). The second energy component associated with hydraulic fracturing is the estimate of hydraulic fluid additives. Fracturing fluid is specific to each individual well. Jiang et al. (2011) provide a generalized estimate of four primary compounds associated with typical multistage fracturing activity, which include: (1) (EIA 2015) proppant; (2) petroleum distillate; (3) inorganic matter; and (4) organic matter. The dollar cost associated with each primary compound is calculated based on individual component inputs (generalized from industry data) and valued at current market prices (Jiang et al. 2011). Based on energy intensity ratios, the CMU model calculates the energy equivalent of additive production at 847.50 GJ (Table 5). In total, the energy cost equivalent per well per fracturing job within the hydraulic fracturing process stage is 1.5 TJ (652.66 GJ + 847.50 GJ).

Table 4 Fuel and electricity requirements for management of Fracking fluid Full size table

Table 5 Hydraulic fracturing additives Full size table

Well Workover

The number of workovers required on an individual well depends on the geology of the well, well depth and length, the rate of production, and the time horizon of the producing well (Clark et al. 2011). Well workovers may include additional hydraulic fracturing jobs, cleaning of the casing, or possibly the installation of new production tubing. Because of the nascent nature of the industry, and uncertainty surrounding the time horizon for production in shale gas wells in the Marcellus Shale that use high-volume hydraulic fracturing (HVHF), it is difficult to project well workovers with much certainty. According to Clark et al. (2011), the industry estimates two workovers per well over its lifetime, and in this case workover refers to re-fracking the well and installation of new production tubing. As a proxy for the installation of new production tubing, the model includes the direct material (less drilling mud) energy costs calculated in the well drilling boundary stage. The total direct material energy costs associated with the well workover is 12.06 TJ. In total, the well workover process stage energy cost equivalent is 13.57 TJ.

Recovery Pipeline

The estimation of energy costs within the recovery pipeline focuses on the installation of the pipeline that feeds into the compressor stations and is adapted from the Clark et al. (2011) study. The material costs considered in pipeline construction are steel and diesel fuel consumption. The amount each is utilized in construction is a function of average EUR, where steel is measured in Mg/MMBtu and diesel fuel consumed at a rate of gal/MMBtu. At an average EUR of 3.16 Bcf, the energy cost of steel utilized is 2.21 TJ, while the energy equivalent of diesel fuel consumption is 74.74 GJ. The total energy costs realized in construction of the recovery pipeline is 2.29 TJ.

Gas Processing

Natural gas produced at the wellhead must be processed before it can be delivered to long distance pipelines. The processing of wellhead natural gas generally involves gas–oil separators, condensate separators and dehydration (to remove free water from the gas), contaminant removal, nitrogen extraction, and methane separation and fractionation (to process and separate natural gas liquids) (NETL 2016). The National Renewable Energy Laboratory (NREL) provides the energetic (fuel) inputs for the processing natural gas at the plant (NETL 2016). The inputs include combusted diesel fuel, electricity utilized from the grid, gasoline combustion, and natural gas and residual fuel oil combusted in the boiler. The fuel sources utilized in the processing stage are reported as an energetic cost (MJ) to treat a cubic feet of natural gas, the processing stage represents a variable energetic cost, and as such, it is a function of estimated EUR. Processing costs range from 438 GJ for the low estimate (0.23 Bcf) to 11.5 TJ for the high estimate (6.03 Bcf), and the mean EUR of 3.16 Bcf represents a cost input of 6.01 TJ.

Gas Transport

Stephenson et al. (2011) estimate gas transport from the processing plant to the power plant at 900 miles. The average compressor station in the pipeline network is spaced every 100 miles, and eight intermediate stations are needed to transport the gas (Stephenson et al. 2011; EIA 1996). The EIA, in 2009 (EIA 2009), estimates that 1.4 % of the natural gas transported through the pipeline is consumed as fuel at the compression stations. Natural gas combustion during gas transport, assuming the mean EUR value of 3.16 Bcf, is 40.11 TJ.

Power Station

According to the EIA (2009) and data used by Stephenson et al. (2011) for power generation, it assumes that natural gas is burned at an average US power station, which by 2009 the efficiency had increased to 43 %. However, natural gas power plant efficiencies range from 28 to 58 % across the USA (Stephenson et al. 2011). Using the average efficiency value and the mean estimated EUR of 3.16 Bcf, 57 % of the average well’s energy content is lost through power generation, equating to an energy cost of 1633.19 TJ.

Calculation of EROI

Utilizing a hybrid LCA approach, we calculated lifetime EROI values using three distinct process stage boundaries (Fig. 4):

Fig. 4 Process stages in the production process Full size image

1. EROI P&P which includes all energy costs through production and local/on-site processing (Eq. 1); $${\text{EROI}}_{{{\text{P}}\& {\text{P}}}} = \frac{{F_{g1} - W_{1} }}{{\left( {X_{e1} + X_{c1} } \right)}}$$ (1) 2. EROI P,P&T which includes all costs through production, local/on-site processing and the cost of transporting that gas to a power plant (Eq. 2), and $${\text{EROI}}_{{{\text{P}},{\text{P}}\& {\text{T}}}} = \frac{{F_{g2} - W_{2} }}{{\left( {X_{e1} + X_{c1} } \right) + \left( {X_{e2} + X_{c2} } \right)}}$$ (2) 3. EROI GRID which includes all energetic costs up to and including the cost to convert natural gas into electricity at a power plant (Eq. 3). $${\text{EROI}}_{\text{GRID}} = \frac{{F_{g3} - W_{3} }}{{\left( {X_{e1} + X_{c1} } \right) + \left( {X_{e2} + X_{c2} } \right)}}$$ (3)

Energy Cost Scenario Analysis

We performed a sensitivity analysis for the inputs according to process stages for our EROI calculations (Figs. 5, 6). For each of the process stages, we adopted minimum and maximum energy input (MJ/MJ) values based on Yaritani and Matsushima (2014) and conducted a Monte Carlo simulation, following a triangular distribution of minimum, mean, and maximum values. The minimum, mean, and maximum energetic input values were used to calculate a range of EROI results based on the estimated mean EUR value.

Fig. 5 Sensitivity analysis (input costs, less electricity production) Full size image

Production Scenario Analysis

Based on our decline curve analysis, we have generated low, average, and high production scenarios utilizing the mean cost scenarios obtained from the sensitivity analysis. The low scenario represents 0.23 Bcf EUR and includes 9 % of the sampled wells falling less than one standard deviation from the mean EUR value. The mean EUR scenario includes all those sampled wells within one standard deviation from the mean (3.16 Bcf), and 79 % of the wells included in the analysis fall within one standard deviation from the mean EUR value. The high scenario EUR is 6.03 Bcf and includes 12 % of the sampled wells.