Conference Paper

NREL/ CP-6A2-47458

February 2010

Updated U.S. Geothermal

Supply Curve

Chad Augustine and Katherine R. Young

National Renewable Energy Laboratory

Arlene Anderson

U.S. Department of Energy

Presented at Stanford Geothermal Workshop

Stanford, California

February 1, 2010
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1

UPDATED U.S. GEOTHERMAL SUPPLY CURVE

Chad Augustine

National Renewable Energy Laboratory 

1617 Cole Blvd.

Golden, CO, 80401-3305, United States

e-mail: Chad.Augustine@nrel.gov

Katherine R. Young

National Renewable Energy Laboratory 

1617 Cole Blvd.

Golden, CO, 80401-3305, United States

e-mail: Katherine.Young@nrel.gov

Arlene Anderson

U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) 

Geothermal Technologies Program (GTP) 

1000 Independence Avenue, SW 

Washington, DC 20585, United States

e-mail: Arlene.Anderson@ee.doe.gov

This paper documents the approach used to update 

the U.S. geothermal supply curve. The geothermal 

supply curve analysis undertaken in this study 

estimates the supply of electricity generation 

potential from geothermal resources in the United 

States and the levelized cost of electricity (LCOE), 

capital costs, and operating and maintenance costs 

associated with developing these geothermal 

resources. The supply curve data are used as input to 

annual reporting by the U.S. Department of Energy

(DOE) under the Government Performance and 

Results Act of 1993, the DOE portfolio development 

support processes, and market penetration models in 

support of other DOE analyses. Supply curves were 

developed for four categories of geothermal 

resources: identified hydrothermal (6.4 GW

ABSTRACT

e), 

undiscovered hydrothermal (30.0 GWe), nearhydrothermal field enhanced geothermal systems 

(EGS) (7.0 GWe) and deep EGS (15,900 GWe). Two 

cases were considered: a base case, which assumes 

modest improvements in EGS reservoir performance 

from current benchmarks, and a target case, which 

assumes significant advances in reservoir 

performance from the Geothermal Technologies 

Program (GTP or the Program) funding of EGS 

research, development, and demonstration projects. 

Project development costs for the geothermal 

resources in the assessment were estimated using the 

Geothermal Electricity Technology Evaluation 

Model (GETEM). Inputs for GETEM were based on 

probability distributions of geothermal technology 

costs and performance levels from experts submitted 

as part of the GTP’s 2009 technical risk assessment. 

Supply curves were generated for each of the four 

geothermal resource categories for both the base and 

target cases. Capital costs by project phase for the 

different technologies were also calculated. For both 

cases, hydrothermal resources dominate the lower 

cost range of the combined geothermal supply curve. 

The supply curves indicate that the reservoir 

performance improvements assumed in the target 

case could significantly lower EGS costs and greatly 

increase EGS deployment over the base case. The 

paper discusses the results of the supply curve 

analysis and improvements that can be made to future 

supply curve representations. 

This paper documents the approach taken as part of 

the Department of Energy (DOE) Geothermal 

Technologies Program’s (GTP or the Program) 

annual supply curve update to characterize and 

represent the supply of electricity generation 

potential from geothermal resources in the United 

States. The geothermal supply curve is used as the 

basis for input to market penetration models for an 

array of tasks that analyze the competitiveness of 

geothermal electricity generation against other forms 

of electricity generation and forecast the penetration 

of geothermal technologies into the national 

INTRODUCTION AND PURPOSE
2

electricity generation market. The primary use of 

data from the supply curve is to provide cost input for 

the annual reporting under the Government 

Performance and Results Act of 1993 (GPRA) and 

for the DOE portfolio development support 

processes. Geothermal supply curve data are also 

supplied as input for numerous additional DOE 

analyses.

The primary purposes of this paper are to: 

1. Document the approach taken in identifying 

geothermal resources and determining the 

electricity-producing potential of these 

resources,

2. Document the approach taken in estimating 

the levelized cost of electricity (LCOE), 

capital costs, and operating and maintenance 

(O&M) costs from these geothermal 

resources, and

3. Discuss the resulting supply curve and how 

improvements can be made to future supply 

curve representations.

For this study, the geothermal resource was broadly 

split between two technologies: conventional 

hydrothermal and enhanced geothermal systems 

(EGS). The hydrothermal resource consists of the 

naturally occurring geothermal sites conventionally 

used to produce electricity. Enhanced geothermal 

systems are artificial geothermal systems created by 

drilling into formations of hot rock, hydraulically 

stimulating the formation to open and extend 

fractures, intersecting the fractures with one or more 

drilled holes, and then circulating fluid through the 

fractures. Injected fluid is heated by the hot rock as it 

is circulated through the reservoir, brought to the 

surface, and then used to produce electricity in a 

power plant before being re-injected into the 

reservoir, forming a closed-loop system. To develop 

the supply curves for this study, the hydrothermal and 

EGS resources were further subdivided into four 

geothermal categories: identified hydrothermal,

undiscovered hydrothermal, near-hydrothermal field 

EGS, and deep EGS. 

In defining the geothermal resource, published and 

available resources were used whenever possible. In 

particular, the supply curve update benefited greatly 

from the geothermal resource assessment performed 

by the U.S. Geological Survey (USGS) in 2008 

(Williams, Reed et al., 2008a). The supply curve 

update also drew upon methodologies and data from 

the Massachusetts Institute of Technology (MIT) 

Future of Geothermal Energy report to characterize 

U.S. EGS resources (Tester et al., 2006). The LCOE 

of the geothermal resources used to generate the 

supply curve were estimated using the Geothermal 

Electricity Technology Evaluation Model (GETEM), 

with cost input elicited from experts as part of a 

recent GTP geothermal technical risk assessment

(Young and Augustine, 2010 (in press)). A more 

detailed account of the methodology and assumptions 

used to develop the geothermal supply curve, 

including additional analysis, is described in a 

forthcoming National Renewable Energy Laboratory 

(NREL) technical report (Augustine, 2010 (in press)).

The same approach for generating a supply curve was 

used for each of the geothermal resource categories

considered. The primary steps in generating a supply 

curve and model input were the resource 

characterization and the estimation of the cost of the 

resource. For the resource characterization, the 

category and scope of the geothermal resource were 

defined. Next, information sources were identified 

and gathered from the literature and other available 

sources. These were assembled into a database of the 

potential electrical generating capacity of the 

resource. The cost of developing each category of 

geothermal resource was estimated using the resource 

characteristics from the characterization, the 

technology components required to develop the 

resource, and any factors or assumptions included in 

the funding case under which the resource would be 

developed. The potential electrical generating 

capacity from the resource characterization was 

combined with the estimated cost of developing that 

capacity to generate the supply curve. 

GENERAL APPROACH AND ASSUMPTIONS

Two cases based on EGS reservoir technology 

advances were considered when developing supply 

curves: (1) the “base” or “no-funding” case, and (2) 

the “target” or “funded” case. The cases were driven 

by input required of all DOE energy technology 

programs for annual GPRA reporting, which analyzes 

the benefits of research, development, and 

demonstration (RD&D) funding for DOE programs. 

Technology assumptions are based on major 

performance goals for EGS in the Program’s MultiYear Research, Development and Demonstration

(MYRD&D) plan (Geothermal Technologies 

Program, 2008). By 2015, the Program plans to 

demonstrate the ability to create an EGS reservoir 

capable of producing 5 MW

Supply Curve Cases

e. By 2020, the Program 

plans to validate the ability of such a reservoir to 

sustain 5 MWe of power generation over a 5-year 

period. In the base case, expensive EGS 

demonstration projects were assumed too risky for 

private industry to undertake on a large scale, so only 

modest improvements are made in EGS reservoir

performance from current benchmarks. In the target 

case, it was assumed that GTP funding of EGS 

RD&D projects enabled MYRD&D goals to be met, 

indicating that significant advances are made in EGS 

reservoir technology. The assumptions in each case 

apply to three reservoir engineering EGS enabling 
3

technology performance metrics (TPMs) identified as 

part of the Program’s 2009 technical risk assessment

(Young and Augustine, 2010 (in press)): the 

production well flow rate (kg/s), the thermal 

drawdown rate of the reservoir (%/year), and the ratio 

of production wells to injection wells in the reservoir. 

The EGS reservoir technology performance metric 

assumptions, which are summarized in Table 1,

match those used in the risk assessment. A thermal 

drawdown rate of 3.0%/year corresponds to an EGS 

reservoir that must be re-drilled and re-stimulated 

once every 4-6 years, depending on its initial 

temperature, due to temperature declines in the 

produced fluid. An EGS reservoir with a thermal 

drawdown rate of 0.3%/year can produce fluid 

without significant produced-geofluid temperature 

decline over the 30-year lifetime of the power plant 

and does not require re-drilling or re-stimulation of 

the reservoir. 

Table 1: EGS technology performance assumptions 

used in base and target cases.

Enabling Technology Base Case 

Value

Target 

Case

Value

Production Well Flow Rate 30 kg/s 60 kg/s

Thermal Drawdown Rate 3.0 %/year 0.3 %/year

Production/Injection Well 

Ratio 2:1 2:1

GETEM was used to estimate costs for all 

geothermal resources considered in this study. 

GETEM is a Microsoft Excel-based techno-economic 

systems analysis tool for evaluating and comparing 

geothermal project costs. It uses a bottom-up 

analysis to calculate the LCOE and capital costs of 

geothermal and hydrothermal projects based on a set 

of user-specified variables. The user defines the 

resource characteristics (e.g., hydrothermal or EGS, 

temperature, depth), project details (e.g., plant type 

and size, pump types, well productivity), and other 

required parameters. GETEM then calculates the 

individual component costs associated with each 

phase of the project—such as exploration, well field 

development, power plant construction, and O&M 

costs—based on user-defined cost inputs, embedded 

cost and system performance correlations, and cost 

indices to account for the year the project is 

developed. Total project costs are calculated 

assuming a user-defined fixed charge rate for project 

financing. GETEM’s primary output is the LCOE for 

the project, but it also provides the total capital costs 

and a breakdown of capital costs and LCOE 

contributions from the various project phases. 

GETEM was developed for the GTP by Princeton 

Energy Resources International, LLC (PERI) (Entigh, 

2006) in collaboration with researchers at the DOE 

national laboratories and external consultants. This 

study used GETEM Version 2008-A6. The current 

version of GETEM is available for download from 

the GTP Web site (Geothermal Technologies 

Program, 2009a). For this study, a baseline year of 

2008 (the most current available in GETEM at the 

time) was used. 

GETEM and @Risk

Since GETEM is a deterministic model, each set of 

user inputs results in a single cost output. However, 

input values in this study for several of the key input 

parameters in GETEM come from the 2009 risk 

assessment. This input is in the form of probability 

distributions of technology costs and performance 

levels, so that that there is a range of possible input 

values for some parameters rather than a single 

number. To accommodate these distributions, @Risk 

Version 5.0 software was used. @Risk is a Monte 

Carlo simulation add-in for Microsoft Excel available 

from Palisade Corporation that links directly to Excel 

to add risk analysis capabilities. The Monte Carlo 

simulation computes a probability distribution of the 

LCOE for a geothermal power plant project based on 

the probability distributions of the GETEM inputs. 

For each simulation in this study, 1,000 iterations 

were performed. 

Geothermal component technology data were elicited 

from experts as part of the Program’s 2009 technical 

risk assessment. A team of geothermal experts 

comprised of industry experts, academic researchers, 

national laboratory researchers, and laboratory 

contractors was assembled in January 2009. Experts 

were divided into four geothermal technology areas: 

(1) exploration, (2) wells, pumps, and tools, (3) 

reservoir engineering, and (4) power conversion. The 

experts were trained on the risk assessment process,

and then agreed on EGS and hydrothermal reference 

scenarios on which to base their component 

technology estimates. A summary of the risk EGS 

plant reference scenario is shown in 

Technology Cost and Performance Data from 

Risk Assessment

Table 2. Based 

on their discussions, published literature, and their 

personal knowledge of the geothermal energy 

industry, the experts agreed on the current

distributions (compromised of the high, low, and 

most likely values) for the technology performance 

metrics in their technology area. In all, distributions 

for 10 geothermal TPMs from the risk assessment 

were used for the supply curve study in estimating 

the current LCOE for geothermal resources in

GETEM. These ten TPMs and the corresponding 

mean, 10th percentile, and 90th

Table 3

percentile values of 

the expert input distributions are listed in . A 

thorough description of the risk assessment process,
4

Table 2: 2009 risk assessment EGS reference 

scenario. Summary of reference scenario 

used by experts when determining 

technology performance metric 

distributions. 

Parameter Value

Geothermal Resource EGS

Plant Type Binary, air-cooled

Net Output 20 MW

Resource Temperature

e

225 o

Plant Design Temperature

C

200 o

Well Depth

C

6 km

Production Well Flow Rate 60 kg/s

Thermal Drawdown Rate 0.3%/year

Production/Injection Well Ratio 2:1

the risk geothermal plant reference scenario assumed 

while eliciting expert data, and the results are detailed 

in the Program’s 2009 technical risk assessment 

report by Young and Augustine (2010 (in press)).

The TPM distributions were applied independently as 

input to GETEM in determining the LCOE for 

geothermal resources in current (2008) US$ for both 

the base and target cases, so that only the enabling 

technology assumptions in Table 1 differed for the 

two cases. Wherever possible, the distributions and 

assumptions used by the experts for the reference 

scenarios were used as guidance for inputs to 

parameters and values in GETEM when estimating 

geothermal energy costs in this analysis. However, 

assumptions had to be made when providing input to 

GETEM when (1) the characteristics of the 

geothermal resource for the supply curve differed 

from those assumed for the risk reference scenarios, 

(2) no relevant guidance was provided in the risk 

reference scenarios, or (3) the guidance in the risk 

reference scenarios resulted in unreasonable or 

unrealistic results when applied to the geothermal 

resource in the supply curve. For several TPMs—

well costs, binary system capital costs, binary system 

O&M costs, and brine effectiveness—the cost or 

performance distribution given by the expert was

specific to the power plant in the risk EGS reference 

scenario. To apply these distributions to the wide 

range of resources in the supply curve, it was 

assumed that the distributions given by the experts, 

when properly normalized, were applicable across the 

entire resource. For example, the well cost 

distribution given by the experts assumes a 6-km 

deep well for an EGS project. However, wells for the 

hydrothermal and EGS resources in the supply curve 

range from 0.3 km to 10 km (1,000 ft to 33,000 ft) in 

depth. To determine well costs in the supply curve 

analysis, the expert well cost distribution for the 

6-km well in the risk reference scenario was 

normalized by the GETEM drilling cost correlation 

value for a medium-cost, 6-km well. The well cost 

input in GETEM for a given resource in the supply 

curve was calculated by taking the value from the 

normalized distribution sampled by @Risk during 

simulations and multiplying by the default GETEM 

value for the cost of a well at the depth of the 

geothermal resource being considered. This 

methodology was applied to all the scenario-specific 

TPM distributions mentioned above.

Since expert input was not elicited for flash plants, 

the GETEM-calculated values for flash plant costs 

and brine effectiveness were used. Sites with plant 

design temperatures less than 225 o

C were assumed 

to be binary plants, while those with design 

temperatures of 225 o

C and higher were assumed to 

be flash plants. 

Table 3: Summary of expert-elicited geothermal technology performance metric input. Input shown is expertconsensus present-day values or distributions of TPMs for the risk reference scenario. The mean, 10th

percentile, and 90th

TPM

percentile values of the TPM distributions are listed.

Value

Technology Area Name Units 10th %ile Mean 90th

Exploration

%ile

Non-Well Exploration Costs (EGS) $M 0.42 1.41 2.53

Non-Well Exploration Costs (Hydro) $M 0.51 1.22 2.00

Exploration Well Success Rate (EGS) % 50.0 64.1 83.4

Exploration Well Success Rate (Hydro) % 20.1 34.8 50.0

Well Pumps & Tools Well Drilling/Construction Cost $M 15.0 22.3 30.0

Production Pump Cost (per well) $M 1.0 1.5 2.0

Reservoir Engineering Stimulation Cost (per triplet) $M 2.7 8.4 15.1

Power Conversion

Binary System Capital Cost $/kW 2,200 2,500 2,800

Binary System O&M Cost/Yr ¢/kWh - 2.2 -

Brine Effectiveness W-h/lbm - 9.50 -
5 

When the risk assessment experts met in February 

2009, drilling costs were near historic highs because 

of the scarcity of steel and cement and increased rig 

rental rents caused by high crude oil and natural gas 

prices (which led to increased demand for oil and gas 

drilling). Since drilling costs are a significant factor 

in the overall cost of a geothermal project, changes in 

drilling costs have a significant impact on LCOE. 

GETEM uses the U.S. Bureau of Labor Statistics 

(BLS) Producer Price Index (PPI) to adjust drilling 

cost estimates to the baseline year chosen in the 

model. As Figure 1 shows, drilling costs have 

declined significantly from these recent record highs 

in the past year. When the supply curves in this 

study were generated in late summer 2009, drilling 

costs had decreased significantly from the values 

assumed during the risk assessment, but only 

preliminary values from the PPI were available. 

Based on conversations with geothermal drilling 

contractors, drilling costs for this study were assumed 

to be 30% lower than the 2008 BLS PPI index value 

in GETEM.

0

50

100

150

200

250

300

350

400

Drilling Cost PPI

(Dec-1985 = 100)

Month

Figure 1: BLS drilling cost PPI. Index values from 

August 2009 onward are preliminary

(Bureau of Labor Statistics, 2009). 

For this study, two types of geothermal resources 

were considered—hydrothermal and EGS. The 

hydrothermal resource was subdivided into two 

categories: identified sites and “undiscovered” 

resources. The EGS resource was also subdivided 

into two categories: the near-hydrothermal field EGS 

resource and the deep EGS resource. Supply curves 

were generated for each of the four geothermal 

resource categories using project costs estimated with 

GETEM. The supply curves are in 2008 US$. 

SUPPLY CURVES

Geothermal Resource 

Identified Hydrothermal Sites

The USGS 2008 geothermal resource assessment

(USGS 2008 assessment) identifies 241 moderateand high-temperature (>90 o

C) sites on private or 

accessible public lands in the United States 

(Williams, Reed et al., 2008a). The methodology 

used to estimate the recoverable energy from each 

site in the 2008 USGS assessment is similar to that 

used in the previous USGS Circular 790 assessment

(Muffler and Guffanti, 1979), and is described in 

Williams, Reed et al. (2008b). To account for 

uncertainties in the estimate of the potential electric 

power generation, Monte Carlo simulations were 

performed. For each site, triangular distributions of 

the probable reservoir volume and temperature were 

made using estimates of the minimum, maximum, 

and most likely values for these parameters. The 

result of the Monte Carlo simulations is a distribution 

of probable electric power generation potential for 

each site. Using this methodology, the USGS 2008 

assessment predicts a mean total of 9,057 MWe of 

power generation potential from identified 

hydrothermal systems on private or accessible public 

lands, with a 95% probability of only 3,675 MWe and 

a 5% probability of up to 16,457 MWe being 

available (Williams, Reed et al., 2008a). This total 

mean value is significantly lower than the 

23,000±3,400 MWe potential from only 52 identified 

sites listed in the USGS Circular 790 assessment 

(Muffler and Guffanti, 1979). The primary reason 

for this decline is a change in the assumed recovery 

factor for geothermal systems. The Circular 790 

assessment assumed an average recovery factor of 

0.25, based on experiences at the Geysers geothermal 

field in California, whereas the USGS 2008 

assessment used a uniform distribution ranging from 

0.08 to 0.20 (0.10 to 0.25 for sediment-hosted 

reservoirs) based on more recent experiences from a 

large number of sites. Additionally, the 2008 

assessment assumes reservoir volumes that are 

smaller for some of the large hydrothermal sites and

temperatures that are lower at several sites compared 

with values used in Circular 790, contributing further 

to the apparent reduction in the overall power 

producing potential (Williams, 2009a). 

The total mean value of 9,057 MWe for the 

recoverable electric power generation potential from 

the USGS 2008 assessment was adopted as the 

starting point for the identified hydrothermal resource 

in this supply curve analysis. The site-specific data 

for the identified hydrothermal resources were 

obtained from the USGS (Williams, 2009b). A cutoff temperature of 110 o

C was adopted because of 

limitations in the range of power plant operating 

temperatures validated in the GETEM code. This 

results in the removal of 106 identified hydrothermal 

sites representing 460 MWe of power producing 

potential. The USGS 2008 assessment does not 

exclude currently installed generating capacity at 

hydrothermal sites. A review of data from the U.S. 

Energy Information Administration (Energy 

Information Administration, 2009) and the 

Geothermal Energy Association (Geothermal Energy 

Association, 2009) databases found 2,480 MWe of 
6

installed summer hydrothermal capacity. Some sites, 

such as the Geysers in California, have a greater 

existing production capacity than the mean potential 

capacity, so their potential was completely removed 

from the supply curve analysis. When current 

capacity and sites with temperatures <110 o

C are 

excluded from the USGS 2008 mean power 

producing potential, the subsequent remaining mean 

potential capacity used in this study for identified 

hydrothermal sites in the United States is 6,394 

MWe.

In addition to identified hydrothermal resources, the 

USGS 2008 assessment also estimated the power 

production potential from undiscovered hydrothermal 

resources. The undiscovered resource was estimated 

by using GIS-based statistical methods to analyze the 

correlation between spatial datasets of geological 

factors that are indicative of geothermal resources 

(e.g., heat flow and magmatic activity) to determine

the probability of the existence of geothermal 

resources in unexplored regions (Williams and 

DeAngelo, 2008; Williams, Reed et al., 2008a). The 

undiscovered geothermal resource power generation 

potential from the 2008 assessment has a mean value 

of 30,033 MWe, with a 95% probability of 7,917 

MWe and a 5% probability of 73,286MWe. For this 

supply curve analysis, the mean value (30,033 MWe

LCOE Estimates

) 

was used.

The present-day LCOE in 2008 US$ for the 

identified hydrothermal resource was estimated using 

GETEM on a site-by-site basis. First, site-specific 

resource definitions were input into the GETEM 

model. The reservoir temperature and capacity were 

obtained from the USGS 2008 assessment (Williams, 

2009b). The net power sales from the plant in 

GETEM were set equal to the potential capacity of 

the identified hydrothermal site. The plant size was 

capped at 100 MWe. The reservoir depth and 

production well flow rates for each site were not 

included in the USGS data. Therefore, flow rates and 

depths used in a previous NREL assessment (Petty 

and Porro, 2007) were adopted. When data were not 

available, a reservoir depth of 1.524 km (5,000 ft) 

and a production well flow rate of 44.2 kg/s 

(350,000 lb/hr) were assumed.

Since the actual resource characteristics of the 

undiscovered hydrothermal resource, such as 

reservoir depth and temperature, are unknown, it was 

assumed that the undiscovered hydrothermal resource 

attributed to each state was similar to the 

hydrothermal resource already identified within each 

state. The identified sites were divided into two 

subgroups in each state—those with reservoir 

temperatures ≥150 o

C and those with temperatures 

<150 o

C—and the mean potential capacity in each 

subgroup was totaled. The undiscovered 

hydrothermal resource in each state was apportioned 

between the designated temperature ranges based on 

the percentage of identified hydrothermal resource in 

each subgroup. The temperature, depth, and flow 

rate of the undiscovered hydrothermal resource were 

determined by calculating the mean-capacity 

weighted average of each of these parameters from 

the identified hydrothermal sites in each sub-group. 

Once the undiscovered hydrothermal resource 

characteristics were defined in GETEM, the process 

and assumptions used to estimate the LCOE were 

nearly identical to those used for the identified 

hydrothermal resources, except that the power plant 

net power output was assumed to be 20 MWe for 

each plant. Also, to account for the added expenses 

of locating and identifying the undiscovered sites, 

exploration costs were assumed to be 150% of those 

for identified hydrothermal resources. 

The cost of power for the identified and undiscovered 

hydrothermal resources was calculated in GETEM 

using the above inputs and expert TPM inputs. For 

all hydrothermal sites, a 3:1 production/injection well 

ratio and a thermal drawdown rate of 0.3%/year were 

assumed. These values are consistent with those at a 

typical hydrothermal power plant. The resulting 

supply curves are shown in Figure 2. The median 

(50th%ile), 10th%ile, and 90th%ile LCOE values 

shown illustrate the range of likely values for the 

hydrothermal power plants given the current state of 

technology based on expert input. Because the base 

and target case assumptions are identical for 

hydrothermal resources, the supply curve is identical 

for both cases.

Geothermal Resource

Near-Hydrothermal Field EGS

The near-hydrothermal field EGS resource consists 

of the areas around hydrothermal sites that lack 

sufficient permeability, in-situ fluids, or both to be 

economically produced as conventional hydrothermal 

resources. These resources require the application of 

EGS reservoir engineering techniques to become 

economic producers of electricity. Because these 

resources are near existing hydrothermal sites, they 

tend to be relatively hot and shallow, and they are 

likely to be the least expensive and first EGS 

resources to be commercially developed. The GTP is 

currently funding EGS demonstration projects to 

develop near-hydrothermal fields at the Geysers, 

California; Raft River, Idaho; Desert Peak, Nevada; 

and Brady Hot Springs, Nevada (Geothermal 

Technologies Program, 2009b), all of which are 

home to conventional hydrothermal power plants. 

A formal assessment of the near-hydrothermal field 

EGS resource has not yet been completed. However, 

if the rock in and around identified hydrothermal 

sites are assumed to have high temperatures but to
7

0

10

20

30

40

50

60

70

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000

Levelized Cost of Electricity (cents/kWh)

Cumulative Capacity (MWe)

Identified Hydrothermal 

Sites

Undiscovered 

Hydrothermal Resource

Figure 2: Supply curve for hydrothermal resource. Present-day median (50th%ile) LCOE estimates in 2008 US$ 

from GETEM for identified and undiscovered resources. 10th%ile and 90th%ile values for each curve 

shown in gray. 

lack sufficient permeability or in-situ fluids to be 

developed commercially, a reasonable estimate of the 

near-hydrothermal EGS resource can be made. 

For this supply curve analysis, it was assumed that 

the difference between the mean and high-end 

estimates of the electricity-generating potential 

capacity for each identified hydrothermal site from 

the USGS 2008 assessment (Williams, Reed et al., 

2008a) represents a part of the reservoir that could be 

made to economically produce electricity using EGS 

reservoir stimulation techniques. When the 

difference between the 5% probability and mean 

power producing potential capacity for each 

identified hydrothermal site in USGS 2008 

assessment sites is taken, and a reservoir cut-off 

temperature of 110 o

C is applied, the estimate for the 

near-hydrothermal field EGS resource is 7,031 MWe

LCOE Cost Estimates

. 

The near-hydrothermal field EGS potential resource 

around the undiscovered hydrothermal resource was 

not considered for this study.

LCOE of the near-hydrothermal field EGS resource 

was estimated on a site-by-site basis in GETEM as it 

was for the identified hydrothermal resource. The 

reservoir temperature and depth for each nearhydrothermal filed EGS site were assumed to be the 

same as those for the corresponding identified 

hydrothermal site. The plant net power sales were set 

equal to the potential power capacity calculated for 

each site. Although the resource characteristics for 

each site were the same as for the hydrothermal case, 

the resource type in GETEM was designated EGS, so 

that well stimulation costs were included. The nonwell exploration costs and exploration well success 

rate for EGS were also used, and a 2:1 production-toinjection well ratio was assumed. The production 

well flow rate and thermal drawdown rate of the 

reservoir were set to the values assumed in the base 

and target cases. The resulting supply curves for the 

base and target cases are shown in Figure 3. The 

10th%ile and 90th%ile LCOE values are shown in 

gray to illustrate the range of likely values for the 

near-hydrothermal field EGS LCOE given the current 

state of technology based on expert input. 

Geothermal Resource

Deep EGS

The deep EGS resource assessment was based on the 

thermal energy stored at depths 3-10 km below the 

Earth’s surface in the continental United States. The 

same volume-based methodology described in a 

previous geothermal assessment performed by NREL 

(Petty and Porro, 2007) was used to determine the 

electricity-generating potential of the EGS resource. 

The supply available was based on the amount of 

thermal energy contained in a volume of rock. Only 

a fraction of this heat can be recovered and carried to 
8

0

20

40

60

80

100

120

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

Levelized Cost of Electricity (cents/kWh)

Cumulative Capacity (MWe)

BASE CASE

TARGET CASE

Figure 3: Supply curve for near-hydrothermal field EGS resource. Present-day median (50th%ile) LCOE estimates 

in 2008 US$ from GETEM for base and target cases shown. 10th%ile and 90th%ile values for each curve 

shown in gray.

the surface by circulating fluid through an engineered 

reservoir. This fraction is defined by the recovery 

factor, Rg. Based on the results of a modeling study 

of flow in fractured systems (Sanyal and Butler, 

2005), a conservative value of Rg, = 20% was 

assumed. This recovery factor lies at the upper end 

of that assumed by the USGS for hydrothermal 

resources in their 2008 assessment (Williams, Reed 

et al., 2008a; Williams, Reed et al., 2008b). The 

allowable temperature decline of the produced 

geofluid from the reservoir over its productive 

lifetime—and hence the allowable temperature of the 

rock in contact with the geofluid—is limited by the 

surface power plant equipment. Although the actual 

temperature change will vary according to proximity 

to fractures, the average temperature decline of rock 

affected by circulating fluid was assumed to be 

∆T = 10 o

C before the produced geofluid temperature 

decreases to the point where the reservoir must be 

abandoned. The recovered thermal energy must then 

be converted to electric energy by a power plant at 

the surface. The conversion efficiency of the power 

plant was calculated based on an analysis of binary 

cycle efficiency as a function of geofluid temperature 

by DiPippo (2004). Finally, the potential power 

capacity of the plant was determined by assuming a 

plant lifetime of 30 years.

The deep EGS resource assessment was made using 

temperature vs. depth data obtained from Southern 

Methodist University (SMU) Geothermal Laboratory

(Richards, 2009), featured in MIT’s The Future of 

Geothermal Energy report (Tester et al., 2006). The 

data consist of the maps showing the estimated 

temperatures at depths of 3-10 km in 1-km intervals 

for the entire continental United States. Sufficient 

temperature and depth data to include Alaska and 

Hawaii were unavailable. The thermal energy in 

place was calculated for 1-km thick volumes at 

depths of 3-10 km (centered at 3.5, 4.5, 5.5, 6.5, 7.5, 

8.5 and 9.5 km depths). Temperature data were 

binned in 50 o

C increments ranging from 50-350 o

C. 

The areal extent of each temperature bin at each 

depth was determined from the SMU maps using GIS 

methods. Federally protected and Department of 

Defense (DOD) lands were excluded from the 

assessment. The resulting rock volume for each 

temperature bin at each depth interval was multiplied 

by the corresponding volumetric potential electric 

capacity. The resulting EGS electricity potential for 

the continental United States for each temperaturedepth combination is shown in Table 4. The 

reservoir is assumed to extend to the bottom of each 

1-km slice, so that the resource estimate for the rock 

centered at 3.5 km has a reservoir depth of 4 km. 

The resource assessment identified 15,908 GWe of 

electricity producing potential, although the amount 

of this resource that can be economically produced is 

likely much smaller.
9

Table 4: Potential electric capacity (MWe

Potential Electric Capacity (MW

) of deep 

EGS resource for continental U.S. by 

temperature-depth combination.

e)

Resource Temperature (o

150-200

C)

200-250 250-300 300-350 >350

Reservoir Depth (km)

4 91,516 117 0 0

5 590,763 26,526 134 0 0

6 1,139,749 227,969 7,680 50 0

7 1,337,049 723,692 86,057 631 0

8 1,539,597 1,129,434 345,285 32,964 320

9 1,881,116 1,159,750 761,653 138,204 9,922

10 1,907,066 1,251,474 1,015,937 433,749 69,298

This assessment estimates a much larger deep EGS 

resource than the 518-GWe estimate reported in the 

USGS 2008 assessment (Williams, Reed et al., 

2008a). The USGS 2008 assessment considered only 

11 states in the western United States and only depths 

between 3km and 6 km, whereas this resource 

assessment included the entire continental United

States (48 states) and depths between 3 km and 10 

km. Most of the roughly 16,000-GWe

LCOE Cost Estimates

deep EGS 

resource reported here is attributed to heat stored at 

depths >6 km. 

The LCOE of the deep EGS resource was estimated 

using GETEM for each temperature-depth 

combination listed in Table 4. First, the resource was 

defined in GETEM for each combination. The 

reservoir and well depths both were assumed to 

extend to the full depth of each 1-km slice, so that the 

3-4 km region is assumed to have reservoir and well 

depths of 4 km. Since the temperatures in each 

interval tend to be skewed toward lower values, the 

reservoir temperature was assumed to be 12.5 o

C (1/4 

of interval) above the lower end of interval (i.e., 150-

200 o

C temperature interval was assumed to have a 

reservoir temperature of 162.5 o

Table 4

C). The production 

well flow rate and thermal drawdown rate of the 

reservoir were set to the values assumed in the base 

and target funding cases. Once an LCOE was 

estimated for each temperature/depth interval 

combination, the results were coupled to the available 

capacity in to generate the deep EGS 

resource supply curve. The resulting supply curves 

for the base and target cases are shown using a semilogarithmic scale in Figure 4. The graph is truncated 

to the first 1,000 GWe of power capacity. The 

10th%ile and 90th%ile LCOE values are shown in

gray to illustrate the range of likely values for the 

deep EGS power plants in the base and target funding 

cases given the current state of technology based on 

expert input. 

0

10

20

30

40

50

60

70

80

90

100

1 10 100 1,000 10,000 100,000 1,000,000

Levelized Electricity Cost (cents/kWh)

Cumulative Capacity (MWe)

BASE CASE

TARGET CASE

Figure 4: Supply curve for deep EGS resource using semi-logarithmic scale. Present-day median (50th%ile) LCOE 

estimates in 2008 US$ from GETEM shown for base and target cases. 10th%ile and 90th%ile values for 

each curve shown in gray. Supply curve truncated to the first 1,000 GWe of potential power capacity.
10

This supply curve analysis considered only 

conventional hydrothermal and EGS technologies 

and did not address all geothermal technologies that 

can be used to produce electricity. In particular, 

electricity generation potential from fluids coproduced during oil and gas production, from 

abandoned oil and gas wells, and from 

“geopressured” resources was not considered. The 

co-produced fluid resource estimate in the last NREL 

assessment (Petty and Porro, 2007) was based on the 

volume of water produced during oil and gas 

production (Curtice and Dalrymple, 2004) and 

electricity generating potentials from the MIT report 

based on a range of assumed co-produced fluid 

temperatures (Tester et al., 2006, pp. 2-29, 2-48), not 

actual temperature data. Also, (Petty and Porro, 

2007) triple-counted the size of resource by treating 

the different temperature assumptions in the MIT 

report as individual resources. The authors of this 

analysis felt that there was insufficient data to make 

reasonable estimates of the co-produced and 

geopressured geothermal resources. An effort to 

perform an accurate assessment of the co-produced 

fluid geothermal resource is planned and will be 

included in future supply curve updates.

Co-Produced and Geopressured Resources RESULTS AND DISCUSSION

A summary of the results of the geothermal resource 

characterization included in the supply curve is given 

in 

Summary of Results

Table 5. Although estimates of the geothermal 

resource were made using the best available data, 

future estimates will likely differ as new 

hydrothermal sites are discovered and better data and 

methodologies become available for estimating the 

capacity of the geothermal resources. Future supply 

curve analyses will also be aided by the new National 

Geothermal Data System (NGDS), which was funded 

under the American Recovery and Reinvestment Act 

of 2009. The goal of the NGDS project is to assess 

and classify all geothermal resources and facilitate 

access to geothermal data sets for developers to lower 

the risk associated with the development of 

geothermal projects. 

The supply curves for the separate geothermal 

technologies were combined to produce a single 

aggregated supply curve for all geothermal 

technologies for the base and target cases and are 

shown in Figure 5. Portions of the supply curves 

overlap because assumptions for identified and 

undiscovered hydrothermal are the same for both 

cases. The supply curves have been truncated to 

show only the first 50 GWe of potential capacity to 

Table 5: Summary of geothermal resource characterization used in the supply curve analysis.

Resource Resource Potential Capacity

Capacity 

(GWe

Source(s) and Description )

Hydrothermal

Identified

Hydrothermal 

Sites

6.39

USGS 2008 Geothermal Resource Assessment

- Identified hydrothermal sites

1

- Sites ≥110 o

- Currently installed capacity excluded

C included

Undiscovered 

Hydrothermal 30.03 USGS 2008 Geothermal Resource Assessment

Enhanced 

Geothermal 

Systems (EGS)

1

NearHydrothermal 

Field EGS

7.03

Assumptions based on USGS 2008 assessment

- Regions near identified hydrothermal sites

1

- Sites ≥110 o

- Difference between mean and 95

C included th

Deep EGS

%ile hydrothermal 

resource estimate

15,908

NREL 2006 Assessment2, MIT Report3, SMU Data

- Based on volume method of thermal energy in rock 3-10 km 

depth and ≥150 

4

o

- Does not consider economic or technical feasibility

C

1 (Williams, Reed et al., 2008a) 2 (Petty and Porro, 2007)

3 (Tester et al., 2006) 4 (Richards, 2009)
11

0

5

10

15

20

25

30

35

40

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000

Levelized Cost of Electricity (cents/kWh)

Cumulative Capacity (MWe)

Deep EGS

Undiscovered Hydrothermal

Near-Hydrothermal Field EGS

Identified Hydrothermal

BASE CASE

TARGET CASE

Figure 5: Aggregated supply curves of the four geothermal technologies analyzed for base and target cases.

emphasize the lowest cost resources that are likely to 

be developed first. The aggregated supply curve 

shows the likely order in which resources would be 

developed based on the LCOE estimated by GETEM. 

Market penetration models consider a wide range of 

factors, such as capital costs, O&M costs, technology 

readiness time frames, future cost multipliers, and 

model-specific assumptions about financing and 

project development times, to calculate and compete 

the costs of developing resources, so the models 

differ slightly from each other and from Figure 5 in 

the order they build out the resources. However, the 

calculated LCOEs of a resource give a good 

approximate measure of the most economical 

resources to build.

Compared to the base case, the target case sees an 

increase in the number of near-hydrothermal field 

EGS projects included in the most cost-effective 

50 GWe

Figure 5

of potential capacity. The target case also 

contains a large amount of deep EGS at a 

significantly lower LCOE than in the base case. The 

large amount of deep EGS capacity shown by the 

purple line in that extends beyond the scale 

of the graph represents the 5km-200o

C depthtemperature combination deep EGS resource. It 

consists of over 25,000 MWe of potential capacity. 

The target case assumes that EGS reservoir 

technology has advanced to the point where 

reservoirs can be reliably engineered with production 

well flow rates of 60 kg/s and thermal drawdown 

rates of 0.3%/year (versus 30 kg/s and 3.0%/year 

respectively for the base case). The large decrease in 

costs in the target case for the EGS projects is due to 

these advances in EGS reservoir performance that 

require fewer wells to be drilled for the power plant 

and no additional costs incurred for drilling and 

stimulating new reservoirs over the lifetime of the 

power plant. 

All geothermal technologies in this supply curve 

analysis, especially EGS, saw higher costs compared 

to those in the previous NREL supply curve analyses

(Petty and Porro, 2007), primarily because of 

increases in drilling costs. (Petty and Porro, 2007)

assumed 2004 drilling costs, which were much lower 

than current drilling costs (see Figure 1). Even 

though drilling costs in this study were assumed to be 

30% less than the 2008 drilling cost index value in 

GETEM, drilling costs were still 64% higher than the 

index value used in 2004. This added significantly to 

the capital costs of geothermal projects. The capital 

costs estimated by GETEM for each of the 

geothermal technologies were broken down into four 

cost groups:
12

1. Exploration/confirmation costs 

• Non-well exploration costs

• Exploration well costs

• Confirmation well costs (two 

confirmation wells, which are later used 

as production wells, are required to prove 

the resource)

2. Drilling costs: Costs of drilling remaining 

injection and production wells

3. Other well-field costs (non-drilling) 

• Injection and production pumps

• Reservoir stimulation costs (for EGS 

sites)

4. Power plant costs: Cost of equipment and 

construction

The breakdown of capital costs follows the phases of 

development for a geothermal project. The 

exploration and confirmation phase carries a much 

higher risk of failure than the later phases (Deloitte, 

2008). Acquiring capital for this early phase before 

the resource is confirmed is more difficult and carries 

a higher cost of capital, usually as equity financing. 

Once exploration and confirmation is complete, the 

success rate of the project increases and capital can 

be acquired at lower interest rates, usually as debt 

financing. 

Capital costs were calculated and are presented for all 

the geothermal technologies and cases in the 

forthcoming NREL full technical report (Augustine, 

2010 (in press)). A breakdown of the capital costs 

for the target case of the deep EGS resource 

temperature/depth combinations in Table 4 is shown 

in Figure 6. Even for deep EGS resources with 

relatively low capital costs, most of the capital costs 

are associated with drilling exploration, confirmation, 

and injection and production wells due to the depth of 

the resource. The stimulation and power plant costs

are relatively small in comparison except for the 

highest quality resources. The target case has 

significantly lower drilling costs than the base case 

because a production well flow rate of 60 kg/s 

(versus 30 kg/s for the base case) is assumed; thus, 

fewer wells need to be drilled for a given power plant 

output. Decreasing drilling costs will be an important 

step in developing the deep EGS resource at a large 

scale in the future. 

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

Capital Costs ($/kW net installed)

Deep EGS Resource Depth-Temperature

Power Plant

Stimulation/Other Wellfield

Drilling

Exploration/Confirmation

Figure 6: Deep EGS resource capital costs for target case. The breakdown of the capital costs by project phase is 

given for each temperature-depth combination listed in Table 4. The lower bound of the reservoir 

temperature in each depth interval was used to identify the resource so that “4k-150C” represents the 

deep EGS resource with a reservoir depth of 4 km and temperature of 150-200 o

C.
13

The supply curve analysis assumes that deep EGS 

resources can be developed at all depths at a given 

location. If a deep EGS resource is developed at one 

depth in a given location, the supply curve does not 

remove the resource at the remaining depths in that 

location from potential development; it assumes that 

if a reservoir is artificially created at 4-km depth, 

another reservoir could still be developed at the same 

location at 5 km, 6 km, 7 km, and so on. Given the 

current state of the technology, this type of 

development is unlikely. The most-economical deep 

EGS resource based on the temperature vs. depth 

profile at a location is most likely to be developed, 

and its presence would preclude the development of 

the remaining resource in that location for the 

lifetime (assumed to be 30 years) of the power plant. 

Optimum Deep EGS Resource Depth

The optimal thickness of the developed depth at a 

given location may vary. As a first look, the 

optimum reservoir depth, assuming a 1-km thick 

reservoir, by location in the continental United States 

was determined based on the LCOE values estimated 

by GETEM for the target case using GIS mapping 

methods. For each point on the map, the LCOE for 

the deep-EGS resources at depths from 3-10 km (in 

1-km increments) were compared, and the 1-km thick 

slice with the minimum LCOE was selected. The 

results were used to create a map to illustrate the 

location of the most cost-effective regions for 

developing the deep EGS resource based on the 

analysis in this study. Regions were grouped by 

favorability, with regions having the lowest LCOE 

being the most favorable and those having the highest 

being the least favorable. By grouping the data, the 

transition between resources is smoothed and the 

classification generalized, so that the results should 

apply even with variations in some of the underlying 

costs or assumptions used in this study. The location 

of the identified hydrothermal sites (and hence the 

assumed near-hydrothermal field EGS resource) was 

also included. The resulting map, shown in Figure 7, 

neatly summarizes the majority of the geothermal 

resources of the United States. The LCOE was not 

assessed in regions where the temperature was not 

above 150 o

C at the maximum considered depth of 10 

km; these locations are indicated in green.

Figure 7: Geothermal resource of the United States. Figure shows the location of identified hydrothermal sites, 

the co-located near-hydrothermal field EGS resource, and the favorability of the deep EGS resource by 

location. The undiscovered hydrothermal resource and other geothermal resources, such as coproduced fluids, are not represented.
14

This geothermal supply curve analysis found that the 

most favorable cost resources have reservoir 

temperatures that are almost entirely between 150o C

and 250 o

C. Although higher temperature reservoirs 

exist, they are at greater depths, which means the 

drilling costs associated with developing them result 

in a higher estimated LCOE. Another interesting 

result was that almost all optimum reservoir depths 

are 5 km or deeper. The deep EGS supply curve 

analysis determined that it was more economic to 

continue drilling beyond 4 km to encounter higher 

temperature reservoirs. 

The results of this analysis are not definitive and 

come with significant caveats. First, the results are 

unique for the EGS reservoir technology performance 

levels assumed in the base and target cases. 

Achieving higher production well flow rates or 

higher production-to-injection well ratios could lower 

drilling costs and change the landscape. Reliable 

expert data on these reservoir TPMs should be 

obtained and used in future supply curves. Second, 

the underlying temperature-depth data used in the 

deep EGS assessment are applied over large areas, 

are not accurate to fine scale, and are based on sparse 

data in some regions. Localized hot spots are likely 

not captured by the data used in the assessment where 

high reservoir temperatures exist closer to the 

surface. Third, a single drilling cost curve was used 

for the entire continental United States. Regions of 

easy or difficult drilling will affect drilling costs and 

change the contour of the maps. A change in drilling 

costs that is due to natural market forces could also 

affect the results of the optimum LCOE analysis. 

Given these caveats, this analysis suggests that future 

deep EGS RD&D should focus on drilling wells 5 km 

and deeper and power plants operating in the 150-

250 o

C range. 

This geothermal supply curve study (Augustine, 2010 

(in press)) updated the geothermal supply curve of 

the United States for use as input into market 

penetration models and GPRA benefits analysis. The 

study established an approach and methodology for 

estimating geothermal resource potential that can be 

easily updated when new resource and cost data 

become available. The resource characterization 

made use of published and available data on 

geothermal resources, in particular the results of the 

USGS 2008 geothermal resource assessment. When 

sufficient information was not available, 

methodologies and assumptions were established for

estimating geothermal resource potential. The results 

of the potential capacity estimates for the four 

geothermal resource categories considered were:

CONCLUSIONS AND RECOMMENDATIONS

• Identified hydrothermal resource (current 

capacity excluded): 6.39 GWe

• Undiscovered hydrothermal resource: 

30.03 GW

 

e

• Near-hydrothermal field EGS resource: 

7.03 GWe

• Deep-EGS resource: 15,908 GWe

Supply curves based on the median, 10th%ile, and 

90th%ile estimates of the LCOE were generated for 

each of the resource categories using GETEM and 

input from the 2009 geothermal risk assessment. The 

individual supply curves were combined to create 

aggregated supply curves for the base and target 

cases. The aggregated supply curves focused on the 

most cost-effective 50 GWe of geothermal resource. 

For the base case, identified and undiscovered 

hydrothermal resources dominate the lower part of 

the curve, with some EGS present at higher LCOE 

values. For the target case, hydrothermal sites still 

dominate the lower part of the curve, but a significant 

amount of near-hydrothermal field EGS resource is 

visible. The cost level at which a large amount of 

deep EGS resource is found in the supply curve is 

significantly lower for the target case than in the base 

case, indicating that meeting GTP technology goals 

could have a significant impact on deep EGS 

deployment and that research should focus on EGS 

reservoir engineering improvements. The results of 

the target case are heavily dependent on the EGS 

reservoir technology performance metric assumptions 

used: 60 kg/s production well flow rate, 0.3%/year 

thermal drawdown rate, and 2:1 production/injection 

well ratio. GIS tools were used to identify and map 

the favorability of the deep EGS resource by location. 

Capital costs by project phase for the different 

technologies were also calculated. For the deep EGS 

resource, drilling costs are the dominant component 

of the total capital costs. LCOE and capital costs 

were generally higher for all geothermal resources in 

this assessment than in the last NREL study (Petty 

and Porro, 2007) mainly because of a significant 

increase in drilling costs over the past several years. 

The previous study assumed 2004 drilling costs, 

while this study assumed that drilling costs were 30% 

lower than the 2008 drilling cost PPI value in 

GETEM based on conservations with geothermal 

drilling companies. Even at this lower value, the 

index value used to adjust drilling costs in GETEM 

was 64% higher than its 2004 value. The results of 

the analysis are heavily influenced by the 

assumptions made for the base and target case, the 

current drilling cost trend, and the accuracy of the 

resource assessments. 

There is ample room for improvement in quantifying 

the geothermal resource as the quantity and quality of 

geothermal resource data continue to increase. Both 

the undiscovered hydrothermal and nearhydrothermal field EGS resource assessments rely 

heavily on assumptions. The deep EGS resource is 
15

based on data that are sparse in many parts of the 

country. Additional efforts are needed to better 

characterize these resources. A co-produced fluid 

resource assessment is also needed. Much of these 

data will be gathered as part of the National 

Geothermal Data System, resource assessments, and 

a classification project recently funded by the GTP 

and can be used in future supply curves. General 

recommendations for improvements to the supply 

curve cost estimates are to improve expert input 

during the risk assessment process, especially for 

EGS reservoir TPMs and the application of the expert 

input to the full range of geothermal resources, the 

inclusion of resource uncertainty measurements in 

the supply curve, and the development of drilling cost 

models that take into account local lithology and well 

diameter. 

Augustine, C. (2010 (in press)), "Updated U.S. 

Geothermal Supply Curve for Market 

Penetration Model Input." NREL/TP-6A2-

47459. Golden, CO. National Renewable Energy 

Laboratory.

REFERENCES

Bureau of Labor Statistics. (2009), "Producer Price 

Index Industry Data: Drilling Oil, Gas, Dry, or 

Service Wells: PCU21311121311101." 

http://www.bls.gov/data/. Accessed December 

28, 2009.

Curtice, R. J. and Dalrymple, E. D. (2004), "Just the 

Cost of Doing Business?" World Oil, 225(10), 

77-78.

Deloitte. (2008), "Geothermal Risk Mitigation 

Strategies Report." http://www1.eere.energy.gov/ 

geothermal/pdfs/geothermal_risk_mitigation.pdf.

DiPippo, R. (2004), "Second Law Assessment of 

Binary Plants for Power Generation from LowTemperature Geothermal Fluids." Geothermics, 

33, 565-586.

Energy Information Administration. (2009), "Form 

EIA-860 Database Annual Electric Generator 

Report." U.S. Department of Energy, 

http://www.eia.doe.gov/cneaf/electricity/page/

eia860.html. Accessed December 28, 2009.

Entigh, D. J. (2006), "Introduction to the Geothermal 

Electricity Technology Evaluation Model 

(GETEM)." Rockville, MD. Princeton Energy 

Resources International.

Geothermal Energy Association. (2009), 

"Geothermal Plants." http://www.geoenergy.org/information/plants.asp. Accessed 

June 1, 2009.

Geothermal Technologies Program. (2008), "MultiYear Research, Development and Demonstration 

Plan: 2009-2015 with Program Activities to 

2025." Washington, DC. U.S. Department of 

Energy, Office of Energy Efficiency and 

Renewable Energy, http://www1.eere.energy.gov 

/geothermal/pdfs/gtp_myrdd_2009-complete.pdf.

Geothermal Technologies Program. (2009a), 

"Geothermal Electricity Technology Evaluation 

Model (GETEM), Version 2008-A6." U.S. 

Department of Energy Geothermal Technologies 

Program, http://www1.eere.energy.gov/ 

geothermal/getem.html. Accessed May, 2009.

Geothermal Technologies Program. (2009b), 

"Geothermal Technologies Program Projects." 

U.S. Department of Energy Geothermal 

Technologies Program, http://apps1.eere.energy. 

gov/geothermal/projects/. Accessed November 4, 

2009.

Muffler, L. J. and Guffanti, M. (1979), "Assessment 

of Geothermal Resources of the United States -

1978." Circular 790. Washington, D.C. U.S. 

Dept. of Interior, Geological Survey, 

http://www.geo-energy.org/aboutGE/ 

potentialUse.asp.

Petty, S. and Porro, G. (2007), "Updated U.S. 

Geothermal Supply Characterization." 

Proceedings, Thirty-Second Workshop on 

Geothermal Reservoir Engineering, Stanford 

University, Stanford, CA.

Richards, M. (SMU Geothermal Laboratory) (2009), 

E-mail message to C. Augustine, May 29, 2009.

Sanyal, S. K. and Butler, S. J. (2005), "An Analysis 

of Power Generation Projects from Enhanced 

Geothermal Systems." Geothermal Resources 

Council Transactions, 29.

Tester, J. W., et al. (2006), "The Future of 

Geothermal Energy: Impact of Enhanced 

Geothermal Systems (EGS) on the United States 

in the 21st Century." INL/EXT-06-11746. 

Cambridge, MA. Massachusetts Institute of 

Technology, http://www1.eere.energy.gov/ 

geothermal/future_geothermal.html.

Williams, C. (U.S. Geological Survey) (2009a), 

Personal communication with C. Augustine, 

March 30, 2009.

Williams, C. F. (U.S. Geological Survey) (2009b), 

Personal communication with C. Augustine, 

April 9, 2009.

Williams, C. F. and DeAngelo, J. (2008), "Mapping 

Geothermal Potential in the Western United 

States." Geothermal Resources Council 

Transactions, 32, 181.

Williams, C. F., Reed, M. J., Mariner, R. H., 

DeAngelo, J. and Galanis, S. P. J. (2008a), 

"Assessment of Moderate- and HighTemperature Geothermal Resources of the 
16

United States." Fact Sheet 2008-3082. U.S. 

Geological Survey, http://pubs.usgs.gov/fs/2008

/3082.

Williams, C. F., Reed, M. J. and Mariner, R. H. 

(2008b), "A Review of Methods Applied by the 

U.S. Geological Survey in the Assessment of 

Identified Geothermal Resources." Open-File 

Report 2008-1296. U.S. Geological Survey, 

http://pubs.usgs.gov/of/2008/1296/pdf/of2008-

1296.pdf.

Young, K. R. and Augustine, C. (2010 (in press)), 

"Report on the U.S. DOE Geothermal 

Technologies Program's 2009 Risk Analysis." 

NREL/TP-6A2-46968. Golden, CO. National 

Renewable Energy Laboratory.
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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

February 2010

2. REPORT TYPE

Conference Paper

3. DATES COVERED (From - To)

4. TITLE AND SUBTITLE

Updated U.S. Geothermal Supply Curve

5a. CONTRACT NUMBER

DE-AC36-08-GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

C. Augustine and K.R. Young: NREL

A. Anderson: U.S. DOE

5d. PROJECT NUMBER

NREL/CP-6A2-47458

5e. TASK NUMBER

GT08.3002

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

8. PERFORMING ORGANIZATION

REPORT NUMBER

NREL/CP-6A2-47458

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)

NREL

11. SPONSORING/MONITORING

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service

U.S. Department of Commerce

5285 Port Royal Road

Springfield, VA 22161

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

This paper documents the approach used to update the U.S. geothermal supply curve. The analysis undertaken in this 

study estimates the supply of electricity generation potential from geothermal resources in the United States and the 

levelized cost of electricity (LCOE), capital costs, and operating and maintenance costs associated with developing 

these geothermal resources. Supply curves were developed for four categories of geothermal resources: identified 

hydrothermal (6.4 GWe), undiscovered hydrothermal (30.0 GWe), near-hydrothermal field enhanced geothermal 

systems (EGS) (7.0 GWe) and deep EGS (15,900 GWe). Two cases were considered: a base case and a target case. 

Supply curves were generated for each of the four geothermal resource categories for both cases. For both cases, 

hydrothermal resources dominate the lower cost range of the combined geothermal supply curve. The supply curves 

indicate that the reservoir performance improvements assumed in the target case could significantly lower EGS costs 

and greatly increase EGS deployment over the base case.

15. SUBJECT TERMS

geothermal supply curve; geothermal resources; supply curve data; supply curve analysis; United States; DOE; 

Geothermal Technologies Program; GTP; electricity generation potential; levelized cost of electricity; LCOE; identified 

hydrothermal; undiscovered hydrothermal; near-hydrothermal field enhanced geothermal systems; EGS; EGS 

deployment; reservoir performance; Geothermal Electricity Technology Evaluation Model; GETEM; geothermal 

technology

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL

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OF PAGES

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a. REPORT

Unclassified

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Unclassified

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