A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Updated U.S. Geothermal 

Supply Characterization 

S. Petty 

Black Mountain Technology 

G. Porro 

National Renewable Energy Laboratory

Presented at the 32nd Workshop on Geothermal Reservoir 

Engineering 

Stanford, California 

January 22–24, 2007 

Conference Paper 

NREL/CP-640-41073 

March 2007 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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UPDATED U.S. GEOTHERMAL SUPPLY CHARACTERIZATION 

Susan Petty 

Black Mountain Technology 

444 NE Ravenna Blvd 

Seattle, WA 98115 

spetty@blackmountaintechnology.com 

Gian Porro 

National Renewable Energy Laboratory 

1617 Cole Blvd., M/S 1533 

Golden, CO 80401 

gian_porro@nrel.gov

ABSTRACT

This paper documents the approach taken to 

characterize and represent an updated assessment of 

U.S. geothermal supply for use in forecasting the 

penetration of geothermal electrical generation in the 

National Energy Modeling System (NEMS). This 

work is motivated by several factors: The supply 

characterization used as the basis of several recent 

Department of Energy (DOE) forecasts of geothermal 

capacity is outdated; additional geothermal resource 

assessments have been published; and a new costing 

tool that incorporates current technology, engineering 

practices, and associated costs has been released. 

The approach has two main components: 

• Characterization - estimates the 

development and operating costs for specific 

geothermal reservoir volumes in the 

subsurface identified in existing databases 

and source publications. 

• Representation - groups and generalizes the 

costs in a manner suitable for input into 

NEMS using the Geothermal-Electric Submodule (GES) input file format. 

While the geothermal resource can be considered as a 

single resource with geographically variable 

properties and designated by reservoir or system, for 

convenience, the resulting supply representation 

incorporates five specific resource types: 

hydrothermal flash, hydrothermal binary, geothermal 

fluids coproduced with oil and gas, and two types of 

Enhanced Geothermal Systems (EGS) resource – 

convective EGS associated with hydrothermal 

resources at depths less than 3 kilometers, and 

conductive EGS potential for depths between 3 and 

10 kilometers. This updated supply representation 

extends supply beyond the traditional three Western 

regions considered by DOE in the past by including 

cost and capacity estimates for three midcontinent 

regions. This representation is based on recently 

available updated supply estimates for hydrothermal 

and convective EGS resources, and new estimates for 

coproduced and conductive EGS resources completed 

as part of the MIT study of EGS resource supply and 

costs. 

This new representation comprises 126 GW of 

resource potential nationally: 89 GW across all 

resource types in the Western regions and 37 GW 

mostly from coproduced potential in the non-Western 

regions. The total represented capacity is nearly the 

same for the Western regions as used in previous 

recent DOE Office of Energy Efficiency and 

Renewable Energy (EERE) Government 

Performance and Results Act (GPRA) benefits 

assessments. However, the Western region mix 

among specific resources is different. The updated 

supply features significantly lower levelized cost of 

energy (LCOE) for hydrothermal resources and 

somewhat lower LCOEs for EGS than used 

previously. Further, the inclusion of a significant 

amount of relatively low-cost coproduced resource 

further accentuates these cost differences and 

contributes to a significant increase in the total 

amount of geothermal resource that is likely to be 

technologically and economically accessible in the 

near through midterm period. 

INTRODUCTION

This report documents the approach taken to 

characterize and represent the supply of geothermal 

1
supply for use in forecasting the penetration of 

geothermal electrical generation in the National 

Energy Modeling System (NEMS) and presents the 

resulting supply in comparison to the representation 

used in earlier analyses. This approach enables the 

updated supply information to be incorporated into 

near- to midterm analysis of the competitiveness of 

geothermal electricity generation, including, for 

example, the determination of Government 

Performance and Results (GPRA) benefits for the 

Department of Energy (DOE) Geothermal 

Technology Program (GTP or “the Program”). This 

resource update was used in a specific 

competitiveness study still under review by the 

Program. 

The NEMS Geothermal Electric Sub-module (GES), 

developed in 1992, was first run using data on the 

capital cost of geothermal power from the IM-GEO 

application developed in the late 1980s by a team led 

by Dan Entingh (Entingh et al., 1988). Costed supply 

curves for geothermal power were developed in 1991 

by a team led by Susan Petty (Petty et al., 1992). 

Information for the supply curves was taken from the 

two primary published sources available at that time: 

USGS Circular 790 (Muffler, 1978) and the 

Bonneville Power Authority study (Bloomquist et al., 

1985). Each of the subsequent updates to the 

geothermal sub-module input data, used in both 

GPRA and Energy Information Administration (EIA) 

Annual Energy Outlook (AEO) analyses, were based 

on the original 1992 study data. 

In this paper, the potential electrical output and 

resource characteristics are developed from published 

sources and a comprehensive assessment of 

Enhanced, or Engineered, Geothermal Systems 

(EGS) carried out by a 12-member panel assembled 

by the Massachusetts Institute of Technology (MIT) 

to evaluate the potential of geothermal energy to 

become a major energy source for the United States. 

As indicated in the resulting MIT report, 

“Geothermal resources span a wide range of heat 

sources from the Earth, including not only the more 

easily developed, currently economic hydrothermal 

resources, but also the Earth’s deeper, stored thermal 

energy, which is available almost anywhere. 

Although conventional hydrothermal resources are 

used effectively for both electric and non-electric 

applications in the United States, they are somewhat 

limited in their location and ultimate potential for 

supplying electricity. Beyond these conventional 

resources are EGS resources with enormous potential 

for primary energy recovery using heat-mining 

technology, which is designed to extract and utilize 

the Earth’s stored thermal energy.”1

 EGS methods 

have been tested at a number of sites around the 

 

1

 Tester et al. (2006), pp. 1-2. 

world and have been improving steadily. The MIT 

study also assessed geothermal resources coproduced with oil and gas production. Hot waters 

contained in oil and gas reservoirs, or coproduced 

geothermal resources, are normally considered a 

subset of the identified EGS resources, as 

permeability enhancement is usually required to 

optimize dedicated production separate from oil and 

gas production. Because this paper considers only 

the coproduced resources available from existing oil 

and gas wells, coproduced is considered a resource 

type separate from EGS resources. These resources 

are accessible with current technology and may be 

relatively inexpensive to develop because the wells 

and other resource-related infrastructure are already 

in place. 

While the geothermal resource can be considered as a 

single resource with geographically variable 

properties and designated by reservoir or system, for 

convenience, cost and electrical capacity estimates in 

the updated supply characterization are organized by 

five specific resource types: hydrothermal flash, 

hydrothermal binary, geothermal fluids coproduced 

with oil and gas, and two types of EGS resource –

convective EGS associated with hydrothermal 

resources at depths less than 3 kilometers, and 

conductive EGS potential for depths between 3 and 

10 kilometers. 

The primary purposes of this paper are to document 

the following: 

• The approach taken to updating the 

characterization of the electrical supply 

potential of the various geothermal resource 

types. 

• The approach taken to transforming this 

characterization into a form suitable for use 

as input to NEMS. 

• The resulting supply representation, by 

resource type and region, in comparison to 

that used in earlier analyses. 

GENERAL APPROACH

Figure 1 depicts the general approach, applicable to 

all five resource types, used to characterize and 

represent geothermal supply. The overall process has 

two main components: a characterization activity that 

identifies the development and operating costs for 

specific physical volumes in the subsurface identified 

in existing databases and source publications; and a 

representation activity that groups and generalizes 

these costs in a manner suitable for input into NEMS 

via the GES input file. While the tasks associated 

with these two components are consistent for all 

resource types, specific methods used to accomplish 

each, including assumptions made, differ depending 

2
on the resource type and the available source data. 

These variations by resource type are detailed below. 

In the characterization phase, performed by Black 

Mountain Technology, physical resource 

characteristics, including depth, temperature, and 

aerial extent, are first collected and integrated from 

original sources. These sources vary from databases 

of identified hydrothermal reservoirs to 3-

dimensional slab models of conductive EGS potential 

calculated for each state. These physical resource 

characteristics are first used to calculate heat-in-place 

and recoverable electrical capacity based on specific 

assumptions, including a recovery factor and other 

performance factors. The characteristics and 

calculated capacities are then used as inputs for 

calculating various component development and 

operating costs, ultimately expressed as levelized 

costs of energy (LCOE). 

he Program’s Geothermal Electric Technology 

sed in GETEM reflect market 

osting variables required for input to GETEM are 

T

Evaluation Model (GETEM), a techno-economic 

systems analysis tool for evaluating and comparing 

geothermal project cases, is used to compute costs for 

a set of user-specified input variables that address 

about four dozen project criteria based on a baseline 

profile of input values that reflect current technical 

capabilities and economic conditions. The GETEM 

tool was developed for the Program by Princeton 

Energy Resources International (PERI) (Entingh, 

2006) under oversight by the National Renewable 

Energy Laboratory (NREL). The development of 

GETEM entailed cooperation among key Program 

researchers at the DOE national laboratories and 

external consultants, including Idaho National 

Laboratory (conversion systems, geoscience), Sandia 

National Laboratory (drilling and wellfield systems), 

Lawrence Livermore National Laboratory 

(geoscience), Lawrence Berkeley National 

Laboratory (geoscience), Black Mountain 

Technology (geoscience and reservoir engineering), 

and Livesay Consultants (drilling). While GETEM 

has recently been reviewed by several individuals in 

the geothermal industry, a more formal and extensive 

validation is planned. 

The component costs u

prices for component equipment, material, and 

services as of the third calendar quarter of 2004. 

Some component costs, such as drilling services, 

have increased significantly since that time, due to an 

increase in demand for factor inputs associated with 

oil and gas resource development. For the purposes 

of this study, these increases are assumed to reflect 

short-term supply-demand imbalances. Real long run 

costs are assumed to be in line with the mid-2004 

costs used in GETEM. 

Characterization Representation

-Temperature

-Depth

-Aerial extent

-Type

-Subdivide capacities

-Omit high cost sites

-Assume resource -Constant cost

recovery factors ranges

-Calculate conversion

efficiency

-GETEM -GETEM

-Batch Process -From several

of database points with each

range

Identify sources, 

characterize resource 

and develop database

Calculate electric 

capacity

Calculate base LCOE 

costs

Create LCOE cost v. 

cum. Capacity curves

Aggregate resource 

by EMM region

Generalize LCOE 

supply curves & 

identify "sites"

Determine average 

capital and O&M cost

Separate capacity 

into pots

Adjust "sites" to 

address file limits 

and case

Figure 1. General Approach to Supply Characterization and Representation

C

defined from published sources on a site-by-site basis 

for identified hydrothermal and convective EGS 

resources. For conductive EGS resources, a similar 

volumetric recoverable heat approach is used to 

determine the capacity of this resource. The 

variables used in calculating the recoverable heat for 

these resources, such as depth and temperature, are 

also used as inputs to GETEM for costing. The 

geothermal resource coproduced from oil and gas 

reservoirs is characterized by U.S. state, temperature, 

3
production rate, and location. This resource data was 

used as input to GETEM to determine cost. As an 

example of the output from the characterization 

phase, Figure 2 depicts national hydrothermal supply 

using binary and flash plant technology including 

identified convective EGS sites on the margins of 

hydrothermal systems, with LCOE as a function of 

cumulative electrical capacity, characterized using 

the above component process. 

In the representation phase, formulated by Black 

and capacity points by region and cost level for input site build-out. 

Mountain Technology, NREL, and OnLocation Inc., 

the overall approach generalizes the calculated cost 

into the GES module. The GES module was 

developed based on the form of data available from 

the original study (Petty et al., 1992) which 

represented the hydrothermal supply only for the 

western regions of the United States. Petty et al. 

aggregated individual hydrothermal resources into 51 

composite sites with similar capital and operating 

costs located in a common utility grid region. As a 

result, GES accepts most of its user-specified inputs 

via a file keyed by “site.” In the input file, each “site” 

has the following supply-related attributes: 

• A NEMS Electricity Market Module 

(EMM)/North American Electric Reliability 

Corporation (NERC) region. 

• Costs specified in capital $/kW (in 

exploration, drilling, wellfield, plant 

components) and O&M $/kW (field and 

plant components). 

• Four sub-blocks or potential levels of 

capacity for which multipliers can be 

specified to adjust individual component 

capital costs within the NEMS GES code, 

reflecting cost escalation with progressive 

0

5

10

15

20

25

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000

Capacity (MWe)

LCOE (¢/kWh)

Binary Plants

U.S. Hydrothermal and Convective EGS Supply

Includes resources currently withdrawn from development

• A technology type that references the 

general conversion technology required to 

recover the resource. EIA’s AEO forecasts 

are typically based on two conversion types: 

two-stage flashed-steam and binary. EERE 

GPRA analyses have been augmented with a 

third type: EGS. 

• Annual capital cost multipliers (applied to 

each component of capital cost) by year, 

normally used to reflect industry 

development experience or Program R&Ddriven improvements to costs with time. 

Figure 2. Hydrothermal and Convective EGS Supply for Identified Resources for U.S. The vertical axis 

identifies the estimated cost of power in 2004$ using a fixed charge rate of 12.8% and cannot be 

compared to either wholesale generation prices charged to utilities by independent power 

producers or to actual electricity process paid by consumers. The graph includes 7,300 MWe of 

mostly flash potential on lands currently withdrawn from development (all with an LCOE less 

than 5 ¢/kWh). (Source: Black Mountain Technology). 

Flash Plants

0

5

10

15

20

25

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000

Capacity (MWe)

LCOE (¢/kWh)

Binary Plants

U.S. Hydrothermal and Convective EGS Supply

Includes resources currently withdrawn from development

Flash Plants

4
• Annual capacities build limits by year in 

MW/yr, normally used to reflect limitations 

in the geothermal industry’s ability to add 

capacity. 

To represent the characterized supply information in 

the GES input file, the calculated cost and capacity 

points for a specific resource type are first aggregated 

by EMM/NERC region. The aggregation from state 

to region level involves assigning each individual 

state to a single region (an approximation, as EMM 

region boundaries do not always coincide with state 

borders). The resulting supply curve for the region is 

then visually inspected to identify ranges of capacity 

that can be generalized as a constant LCOE (a series 

of constant cost steps, where each step is represented 

as a “site” in the input file) as depicted for conductive 

EGS resource for CAL (Region 13) in Figure 3. 

Component capital and operating costs are then 

extracted from GETEM output reports for several 

original cost and capacity points associated with each 

step. The averages of the respective component costs 

associated with these points become the cost 

attributes associated with a composite “site.” 

Additionally, for hydrothermal resource types (both 

flash and binary), the recoverable capacity value 

associated with each site is subdivided into the four 

potential capacity groupings based on the resource 

characterization source and some simple 

assumptions, taking advantage of NEMS’ ability to 

assign different cost multipliers to each potential 

level. Capacities for all other resource types are not 

blocked – they are all included in the first potential 

group and are assigned the average cost attributes 

calculated for the corresponding cost step. 

Finally, sites are adjusted to address file limits and 

specific case requirements and included in an actual 

input file. Although not an absolute constraint, a 51-

site limit, based on the maximum number of sites 

used in previous GPRA analyses, was observed in 

constructing the file. The following sites template 

was initially identified for each region: 

• Hydrothermal Flash (distributed across four 

potential levels) 

• Hydrothermal Binary – 1 (lower cost, 

distributed across four potential levels) 

• Hydrothermal Binary – 2 (higher cost) 

• Coproduced – 1 (180°C fluids, lower cost) 

• Coproduced – 2 (140°C fluids, higher cost) 

• Convective EGS 

• Conductive EGS – 1 (lowest cost, high 

temperature/shallow) 

• Conductive EGS – 2 (higher cost, high 

temperature/deep or moderate temperature/ 

shallow) 

Conductive EGS Supply - CAL (EMM Region 13)

10.0

12.0

14.0

16.0

18.0

20.0

22.0

24.0

26.0

28.0

30.0

- 100 200 300 400 500 600 700 800 900 1,000

Capacity (GWe)

Base LCOE (¢/kWh)

Conductive EGS Supply - CAL (EMM Region 13)

10.0

12.0

14.0

16.0

18.0

20.0

22.0

24.0

26.0

28.0

30.0

- 100 200 300 400 500 600 700 800 900 1,000

Capacity (GWe)

Base LCOE (¢/kWh)

Figure 3. Conductive EGS Supply for CAL (Region 13) for LCOE Costs Up To 25 ¢ /kWh (in 2004$). Squares are 

individual cost and capacity points from the supply characterization. Black lines are inferred constant 

costs steps. (Source: Black Mountain Technology)

5
• Conductive EGS – 3 (higher cost, moderate 

temperature/moderate to deep depth) 

Table 1 identifies the sites configured for the 13 

Lower 48 EMM regions, based on the distribution of 

potential geothermal resources. Only six of the 13 

regions are assigned supply (Figure 4): the three 

Western regions traditionally considered in past 

supply characterizations (NWP, RA, and CAL), 

along with three other regions with large geothermal 

generation potential (ERCOT, SERC, and SPP). 

Coproduced resources present in very limited 

supplies in ECAR (1), MAPP (5), and FL (8) are not 

included in the supply representation. In order to 

conform to a 51-site limit, conductive EGS resources 

with costs significantly higher than expected 

competitive levels are not included in input, 

effectively limiting representation to four of the six 

regions, despite the presence of some amount of 

conductive EGS potential in all 13 regions. The 

consolidation of all six represented regions is 

effectively equivalent to a U.S. Lower 48-wide total. 

Western

- Hydrothermal 

- Co-produced 

- Convective EGS

- Conductive EGS Non-Western

- Co-produced

- Conductive EGS

NWP

RA

CAL

ERCOT

SPP SERC

Western

- Hydrothermal 

- Co-produced 

- Convective EGS

- Conductive EGS Non-Western

- Co-produced

- Conductive EGS

NWP

RA

CAL

ERCOT

SPP SERC

Figure 4. Supply Regions and Resource Types for Updated Supply 

Representation. 

Table 1. Geothermal Supply Representation by EMM Region and Geothermal Resource Type. The 

six regions to which supply is assigned are highlighted in gray. 

Supply "Sites" 

EMM 

Region # 

Region 

Abbrev. States # of States 

Hydro. 

Flash 

Hydro. 

Binary 

Coprod. Conv. 

EGS 

Cond. 

EGS Total 

1 ECAR 

IA, KY, 

MI, OH, 

WV 

5 0 0 0 0 0 0 

2 ERCOT TX 1 0 0 2 0 1 3 

3 MAAC DE, MD, 

NJ, PN 4 0 0 0 0 0 0 

4 MAIN IA, IL, 

MO, WI 4 0 0 0 0 0 0 

5 MAPP MN, ND, 

NE, SD 4 0 0 0 0 0 0 

6 NY NY 1 0 0 0 0 0 0 

7 NE 

CT, MA, 

ME, NH, 

RI, VT 

6 0 0 0 0 0 0 

8 FL FL 1 0 0 0 0 0 0 

6
9 SERC 

AL, AR, 

GA, LA, 

MS, NC, 

SC, TN, 

VA 

9 0 0 2 0 0 2 

10 SPP KS,OK 2 0 0 2 0 0 2 

11 NWP 

ID, MT, 

NV, OR, 

UT, WA, 

WY 

7 1 2 2 1 3 9 

12 RA AZ, CO, 

NM 3 1 2 2 1 0 6 

13 CAL CA 1 1 2 2 1 3 9 

Total Lower 

48 48 3 6 12 3 7 31 

West 11 3 6 6 3 6 24 

Non-West 37 0 0 6 0 1 7 

Notes

While a total of only 31 “sites” are designated, two issues resulted in an expansion of the number of “sites” to 51 

in total. First, sites with total capacities greater than 5,000 MW are subdivided and represented as multiple 

smaller-capacity sites in the GES input file, in order to reduce the impact of the annual build limit constraint (this 

approach, applicable to some EGS and coproduced potential, is described briefly in Section 5). Second, some 

additional “dummy” high-cost sites are added to work around an apparent internal array management problem in 

NEMS that sometimes results in premature run termination when geothermal supply in a region is close to being 

completely taken up.

SPECIFIC APPROACH BY GEOTHERMAL 

RESOURCE TYPE

Table 2 identifies the specific sources that provide 

the basis for the physical characterization of 

geothermal resources. These sources are referred to 

individually in the discussion of each resource type 

below (full citations for these sources are included in 

the References section): 

Table 2. Published Sources Used for Physical Characterization of Geothermal Resources. 

Source Resource Types Text 

Reference 

Citation 

Reference 

United Stated Geological Survey (USGS) 

Circular 790: Assessment of Geothermal 

Resources of the United States - 1978 

Hydrothermal, 

Convective EGS USGS 790 Muffler 

(1978) 

Washington State Energy Office for 

Bonneville Power Authority (BPA): 

Evaluation and Ranking of Geothermal 

Resources for Electrical Generation or 

Electrical Offset in Idaho, Montana, 

Oregon and Washington 

Hydrothermal, 

Convective EGS BPA-NW Bloomquist et 

al. (1985) 

GeothermEx for California Energy 

Commission (CEC): New Geothermal 

Site Identification and Qualification 

Hydrothermal, 

Convective EGS PIER Study Lovekin et al. 

(2004) 

Western Governors’ Association (WGA) 

Clean and Diversified Energy Initiative: 

Geothermal Task Force Report 

Hydrothermal, 

Convective EGS WGA WGA (2006) 

Nevada Bureau of Mining and Geology 

(NBMG): Description of Nevada 

Thermal Springs and Wells by County 

Hydrothermal, 

Convective EGS NBMG Garside (1994) 

7
Great Basin Center for Geothermal 

Energy, University of Nevada Reno: 

GEOTHERM Great Basin Geothermal 

Database. 

Hydrothermal, 

Convective EGS GEOTHERM 

Great Basin 

Center for 

Geothermal 

Energy (2005) 

U.S. Geological Survey (USGS) Open 

File Report 99-425: Geothermal Industry 

Temperature Profiles from the Great 

Basin 

Hydrothermal, 

Convective EGS 

USGS 99-

425 

Sass et al. 

(1999) 

MIT for GTP: Impact of Enhanced 

Geothermal Systems (EGS) on the 

United States in the 21st Century 

Coproduced, 

Convective EGS, 

Conductive EGS 

MIT EGS 

Study 

Tester et al. 

(2006) 

While some of the sources provide development cost 

estimates for some of the sites, these cost estimates 

were not used in this study. Instead, costs are 

estimated consistently for all sites and resource types 

using GETEM. Published costs were compared to 

those calculated with GETEM, and some limitations 

were placed on the lower end calculated costs to 

ensure appropriate alignment. In particular, the costs 

for hydrothermal resources identified in WGA are 

generally higher than those calculated with GETEM 

for use in this study, in part because the WGA costs 

reflect the rapid increase in drilling costs seen in late 

2004 and 2005. 

Specific methodologies were used to develop the 

supply information and the site characteristics for 

costing the different types of resources considered in 

this study. Hydrothermal flash and binary and 

convective EGS resources were assessed on a site-bysite basis. For the coproduced fluids, data on the 

capacity and characteristics of resources were taken 

from the recently completed MIT EGS Study. The 

conductive EGS resource was assessed using data 

generated by David Blackwell and Maria Richards at 

the Southern Methodist University (SMU) 

Geothermal Laboratory as part of the MIT EGS 

Study. The following section describes the 

determination of capacities and site characteristics for 

developing supply curves for each of these three 

methodologies. 

Hydrothermal (Binary and Flash) and Convective 

EGS Resources

Updated site characterization data is contained in a 

database that can easily be updated. Sites were 

characterized by latitude and longitude, state, region, 

geologic province, temperature, aerial extent of 

temperature anomaly, depth to reservoir, and 

estimated or measured flow rate. Other information 

is also contained in the database which, while not 

used by the current version of GETEM, may be 

valuable for improving future cost estimates. This 

information includes the types and amounts of 

noncondensable gases present in the geothermal 

fluids, salinity, present state of development, and 

installed capacity. 

More than 220 individual hydrothermal and 

convective EGS sites associated with hydrothermal 

resources were identified from the published sources 

above. Figure 5 displays the map location of all of the 

hydrothermal and convective EGS sites on a Google 

Earth plot. The database was developed starting with 

the USGS 790 sites with temperatures above 110°C. 

In this 1978 study, the USGS designated candidate 

electrical generation sites having temperatures 150°C 

and higher. Since that time, technology 

improvements have made possible generation of 

power from geothermal fluids with temperatures as 

low as 80°C in special circumstances. This report 

adopts a cutoff temperature of 110°C based on 

GETEM code limit, which precludes costing sites 

with lower temperatures. Sites not in USGS 790, but 

included in BPA–NW, were added to this database. 

A number of sites were also added from USGS 99-

425 based on temperature-at-depth data from 

exploration drilling by the geothermal industry 

during the 1970s to 1980s. Additional sites were 

added from NBMG. The PIER Study was consulted 

for updated temperature, depth, and flow rate per 

well information, but no new sites were found in this 

database. Similarly, some additional information was 

obtained from GEOTHERM, but no new sites were 

added. While the site-by-site database is as complete 

as possible based on published sources, there is no 

doubt that additional hydrothermal sites exist. For 

instance, during the course of this study, a reservoir 

was discovered at Blue Mountain, Humboldt County, 

Nevada that is mentioned in the WGA study, but has 

no obvious surface expression and is not included in 

the other published sources. 

8
Figure 5. Hydrothermal and Convective EGS Sites Locations in the United States. Hydrothermal sites 

are shown in blue, convective EGS sites in green. Some of the EGS sites may be overlapped 

by hydrothermal sites due to the large map scale. While the site database and supply 

characterization covered the entire United States., only sites in the Lower 48 regions were 

represented for input to NEMS. 

The approach taken in determining the supply 

available at a site in the database is essentially the 

same for the hydrothermal and EGS resources (both 

convective and conductive). This methodology, 

originally developed by the USGS for Circular 726 in 

1976 and subsequently employed in USGS 790, 

BPA-NW, the PIER Study, and the MIT EGS Study, 

is based on the concept of a volume of rock 

containing a total amount of heat. A fraction, the 

recovery factor, of this heat is potentially 

recoverable. The thermal energy is then converted to 

electric power assuming a rejection temperature, or 

low temperature to which the water will be dropped, 

a conversion efficiency for the power conversion 

cycle, and a time over which the energy is assumed to 

be produced. For USGS 790, the following 

assumptions were made: 

• Recovery factor: 25% 

• Rejection temperature: 40°C 

• Conversion efficiency: depends on 

temperature, ranging between 4.5% and 

6.0% 

• Time period for energy removal: 30 years. 

No geothermal resource has been produced to the 

point where all possible heat has been extracted, so it 

is impossible to determine realistic recovery factors 

from actual data. In the end, it is necessary to rely on 

numerical simulation to predict the final recoverable 

heat from a resource, until there is adequate data on 

performance of heat mining operations to compare to 

these predictions. Numerical simulations by 

Nathenson (1975), Kruger et al. (2000), Pritchett 

(1998), and Sanyal and Butler (2005) determine that 

recovery factors of close to 50% can be expected 

from circulating fluid in a fractured system. This 

report adopts a 25% recovery factor, as limiting this 

recovery in natural systems to half of that predicted 

for an ideally fractured system seems reasonable. 

Any further limits on the heat actually recovered 

9
should come from other considerations not related to 

the resource potential. 

Supply capacities for hydrothermal systems were 

taken from USGS 790, where available. If a site was 

not included in USGS 790, but was included in BPANW, the value calculated in the BPA-NW study was 

used. For sites not included in USGS 790 and BPANW, the same methodology used in these studies was 

used to calculate the power potential. The area and 

thickness of the temperature anomaly were estimated 

based on the data available to calculate a volumetric 

heat-in-place and then a recoverable fraction of that 

heat as power for 30 years. 

The Geothermal Energy Association, an industry 

trade group, conducted a workshop in 2005 for 

presentation to the Western Governors’ Association 

to determine the amount of geothermal capacity that 

might reasonably be expected to be developed over 

the next 10 years. A group of geothermal experts 

pooled their available information on the capacity of 

identified sites, the cost of power from those sites, 

and the time frame over which they might be 

developed. The group assigned short-term 

developable capacities to sites identified in USGS 

790. While these capacities were generally smaller 

than that calculated by the USGS, they represent only 

the portion of the resource that might be developed 

quickly in the near term. However, they do represent 

an assessment of the portion of the resource that 

might be economic in today’s power markets. For 

this reason, capacities from WGA, where available, 

were adopted as the hydrothermal capacities. These 

capacities were then subtracted from the USGS 790 

amounts, and the remainder at that site was costed as 

convective EGS capacity requiring engineering or 

enhancement to develop, thus increasing the capital 

and O&M costs for that portion of the capacity at that 

site. 

A value for flow rate per well is a requirement for 

determining the cost of any geothermal project. 

There is little actual data on this parameter for any of 

the resources in USGS 790, because most of the 

resources have never been drilled and tested. 

However, there is information on flows from hot 

springs for some of the sites. This information was 

used to estimate the potential flow per well based on 

the range and number of springs, i.e., a large number 

of small springs or a large flow from a single spring 

would suggest a permeable reservoir that might have 

high flow rates per well. 

Once the database of sites with temperature, depth, 

flow, aerial extent of the temperature anomaly, and 

supply capacity was developed, the cost of power at 

each site was then calculated using the GETEM 

model and a batch processing code, which calculated 

the costs for the entire database. With this batch 

approach, the costs can be quickly and easily 

recalculated for the entire database if the data for 

some sites is updated, new sites are added in the 

future, or the GETEM code is modified for updated 

costs. 

Conductive EGS Resources

There is little data available on the nature of the rock 

and the thermal resource that rock contains for depths 

greater than 3 kilometers. No systematic assessment 

of the recoverable energy from the heat conducted 

through the earth’s crust under the United States has 

ever been developed prior to recent efforts reflected 

in the MIT EGS Study. This section describes the 

methods used in the referenced study to determine 

the heat-in-place and the recoverable power from the 

conductive EGS resource. 

Most geothermal wells are drilled to depths less than 

3 kilometers. Oil and gas wells may be drilled to 

greater depths, but they are restricted to areas with 

potential for oil and gas production. Absent 

empirical drilling data, the only method available for 

projecting the temperature of the earth at depth is 

through determining the heat that flows through the 

shallow subsurface and then using the nature of the 

earth materials below to project back to a 

temperature-at-depth. Over the course of more than 

25 years, the SMU Geothermal Laboratory, led by 

David Blackwell, has been making measurements of 

terrestrial heat flow across the United States, 

whenever possible. SMU developed a database of 

heat flow measurements and then combined this data 

with knowledge of geology to develop a series of 

maps of temperature-at-depth. The maps represent 

the average temperature throughout a 1 km-thick 

slice beginning at a specified depth. Figure 6 depicts 

the 4.5 km and 6.5 km maps. A thorough description 

of the method used to develop the maps is included in 

Chapter 2 of the MIT EGS Study. 

10
Figure 6. Average Temperature at Depth for U.S. Lower 48, 4.5 km and 6 km. For the 

4.5 km map, areas of special conductive EGS interest (150 °C or higher) are 

outlined in blue. (Source: SMU Geothermal Laboratory, 2005) 

The same basic methodology adopted by the USGS 

and described above was applied. The map areas 

define 1 km-thick volumes at an average temperature. 

The heat-in-place is calculated for each 1 km slice 

using the heat capacity, rock density at that depth, the 

temperature at depth, and the map-derived volume as 

described in the hydrothermal resource section above. 

The rejection temperature assumed for the heat-inplace calculation is the same as that used in 

hydrothermal section. Heat-in-place calculations 

were made for five specific temperatures (150°C, 

200°C, 250°C, 300°C, and 350°C) at six specific 

depths (4 km, 5 km, 6 km, 7 km, 8 km, and 10 km). 

The recoverable energy from an artificially fractured 

volume of rock has never been measured. Numerical 

modeling is the only way to determine what portion 

of the total heat-in-place might be recovered. Longterm production/injection tests conducted for EGS 

systems at Fenton Hill, Rosemanowes, and Soultz 

were used to validate predicted heat recovery from 

models. Modeling is a reasonable method to 

determine the best recoverability factor. Sanyal and 

Butler (2005) have modeled flow in fractured 

systems to determine the sensitivity of the 

recoverable heat fraction to several important 

parameters: rock temperature, fractured volume, 

fracture spacing, fluid-circulation rate, well 

configuration, and post-stimulation porosity and 

permeability. They used a 3-dimensional finite 

difference model and calculated the fraction of the 

heat-in-place that could be mined as these important 

11
parameters were changed. They found that for a

variety of fracture spacings, well geometries, and 

fracture permeabilities, the percentage of heat 

recoverable from a stimulated volume of at least 1 x 

108

 m3 under economic production conditions is 

nearly constant at about 40%, with a range between 

34% and 47%. This recovery factor can be treated as 

independent of any of these parameters, as long as 

the stimulated volume exceeds 1 x 108

 m3, roughly 

corresponding to a block of rock 500 m x 500 m x 

500 m. Because Phase II of the Fenton Hill project, 

the Soultz project (both the shallow and deep 

stimulated volumes), and the Cooper Basin project 

have achieved fractured volumes based on acoustic 

emissions mapping of at least 1 km3

 (1 x 109 m3), this 

volume requirement seems perfectly reasonable to 

achieve. 

With current technology, it is likely that an EGS 

system can stimulate and access a volume of more 

than 108

 m3. However, there is less control on the 

fracture spacing and permeability. For this analysis, 

a value of 20% (half the modeled recovery factor) 

was used to calculate the supply of power 

recoverable from the conductive EGS resource. 

If a flexible energy-conversion system could be 

implemented that could use any temperature of fluid 

to generate electric power or extract usable heat – 

although at varying efficiency – the rock volume 

could be cooled significantly while the same surface 

equipment was used. However, real electricgenerating power plants, heat pumps, and heat 

exchangers are designed for a specific, fairly narrow 

range of conditions. The larger the difference 

between design conditions and actual operating 

conditions, the less efficient the equipment becomes. 

This efficiency impact places a practical lower limit 

on the circulating fluid temperature, and 

consequently, a lower limit on the average 

temperature of the rock in contact with the fluid. This 

latter temperature is referred to as the reservoir 

abandonment temperature. To be conservative, an 

abandonment temperature only 10°C lower than the 

initial rock temperature was used in estimating the 

recoverable energy fraction. If the reservoir rock 

temperature drops only 10°C on average, there would 

be ample energy left in the reservoir for future use 

with replacement equipment designed to operate at 

lower temperatures. This would increase the 

sustainability of the reservoir for the longer term. The 

practice of replacing system components to track 

declining temperatures may either negatively or 

positively impact economics for specific sites, 

depending on long-term normal maintenance and 

replacement costs if reservoir temperatures were stable. 

Following active heat-mining operations, production 

flow and heat removal would cease, allowing rock 

temperatures to fully recover by conduction in less 

than 100 years (Elsworth, 1989). This temperature 

recovery would permit EGS energy recovery to 

operate sustainably into the future. 

To avoid the problems of determining what power 

cycle would be most efficient and what rejection 

temperature would be best to use, this analysis used 

the net thermal cycle efficiency concept (DiPippo, 

2004) and extended it to flash cycles. As with the 

hydrothermal analysis above, a time of 30 years was 

used for this analysis. 

The resource energy potential was calculated on a 

state-by-state basis, reduced to reflect surface 

development constraints (including parks and 

wilderness areas, Department of Defense lands, and 

very high elevations) and then apportioned into the 

EMM/NERC regions. 

The cost of power from each depth and temperature 

was calculated using GETEM and a batch-processing 

code designed for use with the conductive EGS data. 

Because the flow rates for an EGS well are a function 

not only of the geology but also the technology used 

to enhance the permeability, the per-well flow rate 

used for costing was based on the highest rate 

achieved to-date for an EGS project validated with 

long-term testing. The 25 kg/sec achieved at the 

Soultz project was used for all EGS resources, 

including convective resources associated with 

hydrothermal systems. Flow per well is one of the 

primary targets for research in EGS technology. 

Coproduced Resources

Several studies have been based on the occurrence of 

fluids at high temperature in some oil and gas 

producing areas, including: 

• Gulf Coast and Midcontinent: McKenna 

and Blackwell (2005) and McKenna et al. 

(2005) 

• California: DOGGR (2005) 

A more comprehensive analysis of the geothermal 

resource that is actually coproduced with oil and gas, 

based on the work of Curtice and Dalrymple (2004), 

is included in the MIT EGS Study. This documents 

the geothermal fluid produced at temperatures from 

100°C to 180°C on a state-by-state basis (Table 3). 

Calculated capacities in this table assume that all 

reported produced water production, distributed 

among thousands of producing wells, is available for 

use in binary conversion plants, and utilize a rejection 

temperature of 40°C. Supply capacities for the 

coproduced resource used in this report are based on 

three of the four resource temperatures reported in 

the table: 180°C, 150°C, and 140°C. Values for 

140°C and 150°C are combined and treated at 140°C. 

12
Table 3. Water Production and Potential Power Generation from Oil and Gas Operations for Selected States. 

Capacities used in this report are shaded in gray. (Source: Tester et al., 2006) 

State 

Abbr. State 

Total 

Processed 

Water, 2004 

(bbl) 

Water 

Production 

Rate 

(kGPM) 

Flow 

Rate 

(kg/s) 

Power@ 

100°C 

(MW) 

Power@ 

140°C 

(MW) 

Power@ 

150°C 

(MW) 

Power@ 

180°C 

(MW) 

AL Alabama 203,223,404 18 1,026 18 47 64 88 

AK Alaska 1,688,215,358 153 8,522 153 389 528 733 

AZ Arizona 293,478 0.0265 1.4814 0.0267 0.0676 0.0918 0.1274 

AR Arkansas 258,095,372 23 1,303 23 59 81 112 

CA California 5,080,065,058 459 25,643 462 1,169 1,590 2,205 

CO Colorado 487,330,554 44 2,460 44 112 153 212 

FL Florida 160,412,148 15 810 15 37 50 70 

IL Illinois 2,197,080,000 199 11,090 200 506 688 954 

IN Indiana 72,335,588 7 365 7 17 23 31 

KS Kansas 6,326,174,700 572 31,933 575 1,456 1,980 2,746 

KY Kentucky 447,231,960 40 2,257 41 103 140 194 

LA Louisiana 2,136,572,640 193 10,785 194 492 669 927 

MI Michigan 188,540,866 17 952 17 43 59 82 

MS Mississippi 592,517,602 54 2,991 54 136 185 257 

MO Missouri 17,082,000 2 86 2 4 5 7 

MT Montana 180,898,616 16 913 16 42 57 79 

NE Nebraska 102,005,344 9 515 9 23 32 44 

NV Nevada 13,650,274 1 69 1 3 4 6 

NM New Mexico 1,214,796,712 110 6,132 110 280 380 527 

NY New York 1,226,924 0.1110 6.1931 0.1115 0.2824 0.3840 0.5326 

ND North 

Dakota 182,441,238 16 921 17 42 57 79 

OH Ohio 12,772,916 1 64 1 3 4 6 

OK Oklahoma 12,423,264,300 1,124 62,709 1,129 2,860 3,888 5,393 

PA Pennsylvania 18,571,428 2 94 2 4 6 8 

SD South 

Dakota 6,724,894 1 34 1 2 2 3 

TN Tennessee 62,339,760 6 315 6 14 20 27 

TX Texas 12,097,990,120 1,094 61,067 1,099 2,785 3,786 5,252 

UT Utah 290,427,704 26 1,466 26 67 91 126 

VA Virginia 2,235,240 0.2022 11.2828 0.2031 0.5145 0.6995 0.9703 

WV West 

Virginia 252,180,000 23 1,273 23 58 79 109 

13
WY Wyoming 3,809,086,632 344 19,227 346 877 1,192 1,654 

US United 

States 50,525,782,830 4,569 255,041 4,591 11,631 15,814 21,933 

Total US 

Coproduced 

Potential

53,969 

Capital and O&M costs were estimated using 

GETEM for the two temperature fluids. Drilling 

capital costs assume recompletion of existing oil and 

gas producing wells. Included in this recompletion is 

the cost of well stimulation through hydraulic 

fracturing, as well as removal of production tubing 

and perforation through the hottest water zones in the 

well to maximize water production. Costs for the 

wellfield generated by GETEM include the cost of 

piping. The flow rates assumed for the average 

production well for this study are low, 38 kg/sec, 

compared to flow rates for hydrothermal systems. As 

a result, a large number of wells are needed to make a 

30 MW power plant. These factors result in fairly 

high wellfield piping costs. The same assumption for 

plant size, per well flow rate, injection pumping, and 

other wellfield characteristics were used for both the 

140°C and 180°C cases. Because produced fluids are 

assumed to be under normal pressure (i.e., not 

geopressured), additional equipment is not required 

for heat recovery and conversion. Because only two 

discrete temperatures are used, and these fluids are 

assumed already delivered to the surface via existing 

infrastructure, the levelized costs for each of the six 

regions are represented by the same two points, 3.9 

¢/kWh for the 180°C resource and 5.8 ¢/kWh for the 

140°C resource ($2004 calculated using a fixed 

charge rate of 12.8%). 

Treatment of Geopressured Resources

Most studies of high-temperature fluids in oil and gas 

environments have focused on resources in the Gulf 

Coast states. Some of these resources have very high 

reservoir pressures, which add to the energy content 

of the fluids. In addition, high pressures may result 

in the dissolution of substantial amounts of methane, 

which can be produced when the pressure is dropped. 

The heat content of these geopressured resources 

accessible from existing oil and gas wells is included 

in the estimates of coproduced resources. Similarly, 

the heat content of geopressured resources not 

already producing and occurring at depths greater 

than 3 kilometers are considered part of the 

conductive EGS resource estimates. For the purposes 

of this paper, the kinetic energy and dissolved 

methane resource associated with the geopressured 

resource are neglected. 

The above costs and capacities of both coproduced 

and conductive EGS resource are estimated assuming 

normally pressured fluids. Costs and capacities for 

the geopressured component of these resources 

would differ from these initial estimates: Dealing 

with fluid under pressure and methane might require 

additional equipment, which would increase costs; 

while the kinetic energy due to the pressure and 

chemical energy content of any recovered methane 

would serve to increase recoverable energy. 

Supply Representation to NEMS

Specific approaches were employed to aggregate and 

generalize the site and state-specific supply 

characterizations for the hydrothermal and EGS 

resource types. Table 4 identifies the capacity factors 

assigned to the resource types as inputs to NEMS. 

These factors are generally higher than used in 

previous analyses, particularly for hydrothermal 

resources exploited with binary conversion 

technology, and reflect recent operational experience. 

Table 4. Assumed Capacity Factors by Technology 

Type. 

Technology Type GPRA06 Updated 

Supply 

Hydrothermal Flash 0.95 0.90 

Hydrothermal Binary 0.80 0.95 

Coproduced (Binary) NA 0.95 

EGS (Binary) 0.90 0.95 

RESULTING SUPPLY REPRESENTATION

The application of the approaches described above to 

the five types of geothermal resources results in the 

updated supply representation. This representation is 

depicted below in two different forms: summary 

tables that display total capacities by resource type 

and region (Tables 5 and 6) and figures that display 

levelized cost supply curves (Figures 7 and 8). These 

depictions are based on a consistent source: the actual 

base input (prior to any cost improvement resulting 

from industry learning or Program R&D), in the form 

of composite “sites,” used for the GES module. In 

most of the tables and figures in this section, the 

updated supply representation is compared to 

GPRA06 Supply, an earlier representation of 

14
geothermal supply used in the GPRA analysis for the 

FY06 Program budget submission. 

The capacities in Table 5 reflect the totals included in 

the updated supply representation of geothermal 

supply (at various costs) for the five resource types. 

Represented supply in the three Western regions 

totals 89 GW across all resource types, with 18% of 

the capacity having a capital cost below $3,000/kW. 

The three non-Western regions contribute 37 GW, 

mostly from coproduced potential, with 40% of the 

capacity having a capital cost below $3,000/kW. The 

total national resource capacity represented is 126 

GW. 

As seen in Table 6, the total represented capacity is 

nearly the same for the Western regions as that used 

in GPRA previously (all resource types included). 

The hydrothermal component of the total national 

geothermal resource is slightly greater in the updated 

supply, while the EGS component is almost 30 % 

less. The balance is made up by the addition of 

coproduced resources in the updated supply 

representation.

Table 5. Updated Supply Representation of Total Capacities of Geothermal Supply for United States and Regions. 

Total Resource Capacities (GW) 

Non-Western Western 

2 9 10 Sub- 11 12 13 SubERCOT SERC SPP Total NWP RA CAL Total 

US 

Total 

Resource Type 

Hydrothermal (Flash & 

Binary) 0.0 0.0 0.0 0.0 15.3 0.7 11.6 27.6 27.6 

Coproduced 11.8 3.2 18.1 33.2 4.2 1.7 5.0 10.8 44.0 

Subtotal (Base Case) 11.8 3.2 18.1 33.2 19.5 2.4 16.6 38.4 71.6 

EGS (Associated with HT) 0.0 0.0 0.0 0.0 9.0 6.4 9.4 24.8 24.8 

EGS (Conductive) 4.0 0.0 0.0 4.0 23.7 0.0 2.2 25.9 29.9 

Subtotal 4.0 0.0 0.0 4.0 32.7 6.4 11.6 50.7 54.7 

Total (Program Case) 15.8 3.2 18.1 37.2 52.2 8.8 28.2 89.2 126.3 

Cost Characterization 

Resource <$3,000/kW in 

Capital Cost 5.3 1.4 8.1 14.8 8.5 1.0 6.7 16.1 30.9 

Resource < $3,000/kW Share 

of Total 33% 44% 45% 40% 16% 11% 24% 18% 24% 

Resource <$4,500/kW in 

Capital Cost 11.8 3.2 18.1 33.2 51.3 2.4 26.6 80.3 113.4 

Resource < $4,500/kW Share 

of Total 75% 100% 100% 89% 98% 27% 94% 90% 90% 

Table 6. Representation of Total Capacities of Geothermal Supply for United States and Region 

Aggregates, Updated vs. GPRA06 Supply.

Total Resource Capacities (GW) 

Non-Western Western US 

Updated GPRA06 Updated GPRA06 Updated GPRA06 

Resource Type 

Hydrothermal (Flash & Binary) 0.0 0.0 27.6 22.2 27.6 22.2 

Coproduced 33.2 0.0 10.8 0.0 44.0 0.0 

Subtotal (Base Case) 33.2 0.0 38.4 22.2 71.6 22.2 

EGS (Associated with HT) 0.0 0.0 24.8 25.0 24.8 25.0 

EGS (Conductive) 4.0 0.0 25.9 46.5 29.9 46.5 

Subtotal 4.0 0.0 50.7 71.5 54.7 71.5 

Total (Program Case) 37.2 0.0 89.2 91.1 126.3 91.1 

15
Cost Characterization 

Resource <$3,000 in Capital 

Cost 14.8 0.0 16.1 7.2 30.9 7.2 

Resource < $3,000 Share of 

Total 40% 0% 18% 8% 24% 8% 

Resource <$4,500 in Capital 

Cost 33.2 0.0 80.3 83.0 113.4 83.0 

Resource < $4,500 Share of 

Total 89% 0% 90% 91% 90% 91% 

Figure 7 compares the levelized cost (LCOE) of the 

aggregate resources types – hydrothermal, 

coproduced, and EGS – from a national perspective, 

incorporating the contribution of both capital and 

O&M costs. LCOEs for both updated supply and 

GPRA06 Supply representations are computed 

assuming a fixed charge rate of 12.8%. In the updated 

supply representation, coproduced resources 

collectively represent the lowest-cost resources, 

underlying the curves for the other resource 

aggregates, reflecting the assumption that this 

potential can be developed using mostly existing well 

infrastructure. Hydrothermal resources, requiring 

wells to be drilled as part of their development, are 

next in cost for the bulk of their represented capacity. 

The high-cost tail on the hydrothermal curve reflects 

the higher potential cost levels associated with lower 

temperature reservoirs exploited with binary 

conversion technology. 

EGS resources are the highest cost, partly reflecting 

the additional cost of stimulation to improve reservoir 

permeability, but primarily the lower flow rates 

currently achievable with stimulation technology. 

The high-cost tail on the EGS curve is the result of 

including one EGS site for a non-Western region 

(ERCOT). With the exception of ERCOT, the EGS 

resources having capital costs greater than 

$5,000/kW are excluded from the representation, due 

to the voluntary imposition of a site limit on the input 

file. Conductive EGS resources are, in addition, 

generally hampered by high-cost combinations of 

lower-temperature resources at shallow depths, and 

higher-temperature resources only at greater depths. 

In comparison to the GPRA06 Supply representation, 

the updated supply representation features 

significantly lower levelized costs for hydrothermal 

resources and somewhat lower LCOEs for EGS 

resources. The significant amount of relatively lowcost coproduced resource is unique to the updated 

supply representation. In aggregate, the updated 

supply has a lower levelized cost than GPRA06 

Supply throughout the range of cumulative capacity 

addition. The difference is particularly pronounced in 

the first 20 GW of potential additions. This lowercost supply is due largely to technology improvement 

in the efficiency of flash plants, improvements in 

technology for reducing reservoir decline, and in the 

lower costs for O&M due to increased automation. 

16
 Geothermal Supply (US)

Updated v. GPRA06

0

20

40

60

80

100

120

140

160

180

200

- 10 20 30 40 50 60 70 80 90 100 110 120 130

Cumulative Capacity (GW)

LCOE (2004$/MWh)

GPRA06 - Hydrothermal GPRA06 - Total Updated - Hydrothermal Updated - Total GPRA06 - EGS Updated - Co-produced Updated - EGS

Figure 7. Representation of Levelized Cost (LCOE) of Geothermal Supply for the United States, by 

Resource Type. Solid lines reflect the updated supply representation, dashed lines the 

GPRA06 Supply representation used in previous analyses. 

Figure 8 compares the aggregate national total 

geothermal supply for the two supply representations 

(corresponding to “Total” curves in Figure 5) and 

identifies the contributions of the various resource 

types to each. In the updated supply representation, 

hydrothermal (flash and binary) and coproduced 

resources are the least costly, providing roughly 60 

GW of capacity under $60/MWh (6.0 ¢/kWh). EGS 

resources, first convective and then conductive, 

contribute an additional 50 GW costing $60 to 

$80/MWh. By comparison, in the GPRA06 Supply 

representation, hydrothermal (flash and binary) 

resources provide a much smaller 10 GW of capacity 

under $70/MWh, followed by roughly 70 GW of 

EGS resources (convective and conductive) for $70 

to $90/MWh. In this GPRA06 representation, EGS 

resources occupy the middle, generally flat portion of 

the supply curve.

17
Geothermal Supply - Total (US)

Updated v. GPRA06

0

20

40

60

80

100

120

140

160

180

200

- 10 20 30 40 50 60 70 80 90 100 110 120 130

Cumulative Capacity (GW)

LCOE (2004$/MWh)

HT F

HT B

CoP

CoP

EGS Conv

EGS Cond

EGS Conv

HT F HT B

HT F

EGS Conv/ Cond 

HT F/ B 

HT F/ B 

EGS Cond

Figure 8. Representation of Levelized Cost (LCOE) of Total Geothermal Supply for the United States. 

Solid line reflects the updated supply representation, dashed line the GPRA06 Supply 

representation used in previous analyses. The contribution of specific resource types is 

noted where “HT F” refers to hydrothermal flash, “HT B” to hydrothermal binary, “CoP” 

to coproduced, “EGS Conv” to convective EGS, and “EGS Cond” to conductive EGS. 

The updated supply representation, in aggregate, has 

a lower levelized cost than previous supply 

representations and should contribute to a significant 

increase in the total amount of geothermal resource 

that is likely to be technologically and economically 

accessible in the near-term through midterm period. 

RELATED KEY MODEL ISSUES

While the supply representation presented above is 

the primary geothermal input to GES, there are 

several other inputs that either impact the LCOE of 

the supply representation through time or place limits 

on how much supply can be absorbed in a given 

period. 

First, the cost-reduction impact of technological 

change is often modeled as the result of two distinct 

processes: R&D that lowers the cost of specific 

technology components, and industry learning by 

doing (LBD) that reduces overall capital costs as a 

function of increasing cumulative installed capacity. 

NEMS provides mechanisms to separately 

incorporate these effects. Either or both of these cost 

reductions can be applied to the input base supply 

relations to lower supply costs over time. For the 

determination of GPRA benefits, the impact of 

Program R&D is implemented via both capital and 

O&M multipliers. This “program” case is then 

compared to a case where only industry learning is 

assumed. 

As an illustration of this latter Program effect on the 

cost of supply, Figure 9 depicts an example impact of 

potential improvement for both capital and O&M 

costs from Program R&D on the levelized cost of 

geothermal supply for two specific years: 2015 and 

2030. In this example, the bulk of the cost 

improvement is experienced by 2015. The impact of 

Program improvement between 2015 and 2030 is 

attenuated by an increase in the fixed charge rate 

used to compute LCOE (13% in 2015, 13.8% in 

2030). This increase is consistent with NEMS 

assumptions. By 2030, program improvement has 

reduced the levelized cost by $5/MWh (5¢/kWh) for 

the least-expensive resources (up to 25 GW in 

capacity), by $10/MWh for the next tier of supply (an 

additional 35 GW), and by up to almost $20/MWh 

for the remaining 60 GW. The application of program 

improvement also shifts the position of various 

resource types along the supply curve, in particular 

shifting EGS resources to the left. As an example, the 

first block of conductive EGS resources becomes 

equal in cost ($48/MWh) to the more costly block of 

coproduced resources by 2030. 

The GES input file also explicitly incorporates a 

mechanism to provide an upper bound to the amount 

18
of capacity added at each specified site in any one 

year. This mechanism takes the form of an annual 

build limit that is expressed by site and year. While 

this feature provides substantial flexibility, it 

currently replaces an endogenous build control 

mechanism common to other electricity technologies 

in NEMS. This mechanism increases capital costs 

when additions of significant capacity are called for 

in a short period through the application of an input 

growth elasticity factor. 

The supply representation and these additional inputs 

determine the cost and capacity of supply available to 

each region within a given forecast year. Changes in 

these inputs must be coordinated to achieve credible 

forecast results. As an example of this need for 

change coordination, a less costly supply 

representation by itself will not have a significant 

impact on penetration if restrictive build limits are in 

place. Further, if the capacity of supply is increased 

at individual sites in the input file, some 

consideration should be given to increasing the build 

limit proportionally if a modeling goal is to 

understand the impact of the capacity change on 

penetration. Alternately, the same effect can be 

achieved by subdividing high-capacity sites into 

smaller sites while the build limit is maintained. 

Geothermal Supply - Total (US)

Updated

0

10

20

30

40

50

60

70

80

90

100

- 10 20 30 40 50 60 70 80 90 100 110 120 130

Cumulative Capacity (GW)

LCOE (2004$/MWh)

Base Improvement - 2015 Improvement - 2030

HT F

HT B

CoP

CoP

EGS Conv EGS Cond

EGS Conv

HT F HT B

HT F

CoP

CoP

HT F EGS Conv

HT F/B

HT F

EGS Cond

EGS Conv

/B

Figure 9: Example Representation of Levelized Cost (LCOE) of Total Geothermal Supply for the United 

States. (All Resource Types Included). The base updated supply curve from Figure 8 above is 

depicted in dark blue, along with two example “improved” versions (2015 and 2030) that 

reflect potential Program improvement impact on both capital and O&M costs. The 

contribution of specific resource types is noted for the base and 2030 supply curves, where 

“HT F” refers to hydrothermal flash, “HT B” to hydrothermal binary, “CoP” to coproduced, 

“EGS Conv” to convective EGS, and “EGS Cond” to conductive EGS. 

CAVEATS AND LIMITATIONS

The lower cost of geothermal supply reflected in the 

updated supply representation largely results from the 

impact of technology improvements that have 

occurred over the past 20 years, including improved 

efficiency of flash plants, technical gains in reducing 

reservoir decline, and increasing use of automation in 

field and plant operations. This lower-cost supply 

should contribute to a significant increase in the 

amount of geothermal resource that is likely to be 

technologically and economically accessible in the 

near- through midterm (2030) period. 

Consistent with NEMS input requirements, the 

capital and O&M costs identified in the 

representation of geothermal supply do not include 

the cost of development financing (NEMS 

determines financing costs based on its own 

assumptions). For convenience and accessibility, the 

19
represented supply has been depicted above as the 

levelized cost of supply, which does incorporate the 

cost of development financing in the form of an 

assumed fixed charge rate of 12.8%. The depicted 

levelized supply costs cannot be compared to either 

wholesale generation prices charged to utilities by 

independent power producers or to actual electricity 

prices paid by consumers. 

The primary limitation to the approach used to 

construct the updated supply representation is that it 

does not directly reflect the uncertainty associated 

with both costs and capacities. The approach assumes 

that the development of the full represented 

capacities of the various types of geothermal 

resources can be accomplished at the specified capital 

and O&M costs. A preliminary risk analysis indicates 

that these expected capacities and costs are 

conservative. However, given the range of 

uncertainties estimated, the costs and capacities for 

coproduced and EGS resources, in particular, are not 

informed by much actual development and 

operational experience. 

Several existing NEMS limitations were overcome 

by the supply representation approach. Elimination of 

these limitations, primarily in the NEMS application 

code, will reduce the effort necessary to represent 

future supply updates. 

Finally, there are several opportunities for further 

work to refine both the approach and actual updated 

supply representation: 

• Implement the mostly automated process to 

update supply information, responding to 

updates in GETEM baseline cost inputs or 

algorithms and updates to resource 

assessments, and timing it to feed the annual 

AEO and GPRA processes. The data 

structure described above lends itself to 

straightforward updating in the future with 

refreshed technical and economic data. 

• Incorporate the results of an updated risk 

analysis in supply cost and capacity by 

representing for NEMS input updated 

characterizations of the 25% and 75% 

probability supply curves. Alternately, 

identify more conservative alternate supply 

scenarios, particularly for coproduced and 

EGS resources. 

• Investigate the potential to revise the supply 

representation process to identify 

hydrothermal and convective EGS sites as 

distinct “sites” in the GES input file. Also, 

investigate the potential to represent the 

energy capacities and costs of specific 

conductive EGS temperature-depth volumes 

within each state as individual sites. Both 

approaches would serve to eliminate the 

generalization step used to represent supply 

in this analysis and would serve to make the 

GES input file a more transparent reflection 

of the specific supply sources. 

REFERENCES

Bloomquist, R.G. et al, 1985. “Evaluation and 

Ranking of Geothermal Resources for 

Electrical Generation or Electrical Offset in 

Idaho, Montana, Oregon and Washington”. 

DOE Technical Report # DOE/BP/13609-3, 

Washington State Energy Office for 

Bonneville Power Administration, June 

1985. 

http://www.osti.gov/energycitations/servlets/

purl/5632979-icrfaW/5632979.PDF 

Curtice, R. J., and E. D. Dalrymple, 2004. “Just the 

cost of doing business.” World Oil, pp. 77-

78. 

DiPippo, R. 2004. “Second Law Assessment of 

Binary Plants for Power Generation from 

Low-Temperature Geothermal Fluids,” 

Geothermics, V. 33, pp. 565-586. 

DOGGR, 2005. 2004 Annual Report of the State Oil 

& Gas Supervisor, California Department of 

Conservation, Div. of Oil, Gas, and 

Geothermal Resources, Sacramento, p. 197. 

Elsworth, D., 1989. “Theory of thermal recovery 

from a spherically stimulated HDR 

reservoir.” Journal of Geophysical Research, 

94 (B2): 1927 - 1934. 

Entingh, D.J., 2006. Introduction to the Geothermal 

Electricity Technology Evaluation Model 

(GETEM). Princeton Energy Resources 

International, Rockville, MD. 

Entingh, D.J., Petty, S and B. J. Livesay, 1988. IMGEO: Impact of R and D on cost of 

geothermal power: Documentation of Model 

Version 2. 09, Contractor Report Sandia 

National Laboratory, SAND-87-7018, 

February, 1988 

Garside, L., 1994. “Description of Nevada Thermal 

Springs and Wells by County” in Thermal 

Waters of Nevada, Nevada Bureau of 

Mining and Geology (NBMG) Bulletin 91, 

1994. 

Geothermal Technologies Program, 2005. “DOE 

Geothermal Technologies Program MultiYear Program Plan 2006 – 2011”. U.S. 

Department of Energy, Washington, DC. 

20
Great Basin Center for Geothermal Energy, 

University of Nevada Reno, 2005. 

GEOTHERM, Great Basin Geothermal 

Database. 

http://www.nbmg.unr.edu/geothermal/geoch

emdata/geotherm.htm 

Kruger, P., Karasawa, H.,Tenma, N., and K. Kitano, 

2000. “Analysis of heat extraction from the 

Hijiori and Ogachi HDR geothermal 

resources in Japan.” Proceedings World 

Geothermal Congress 2000, pp. 2,677-

3,682, Japan. 

Lovekin, J., Klein, C., and S. Sanyal, 2004. “New 

Geothermal Site Identification and 

Qualification”, Public Energy Research 

Consultant Report #P500-04-051, California 

Energy Commission, April, 2004. 

 http://www.energy.ca.gov/reports/500-04-

051.PDF. 

McKenna, J.R. and D.D. Blackwell, 2005. 

“Geothermal Electric Power from Texas 

Hydrocarbon Fields”, GRC Bulletin, 

May/June, pp. 121-128. 

McKenna, J.R., D.D. Blackwell, C. Moyes, and P.D. 

Patterson, 2005. “Geothermal Electric 

Power Supply Possible from Gulf Coast, 

Midcontinent Oil Field Waters”, Oil and 

Gas Journal, Sept. 5, pp. 34-40. 

Muffler, L.J.P., ed., 1978. Assessment of Geothermal 

Resources of the United States – 1978. U.S. 

Geological Survey (USGS) Circular 790. 

http://onlinepubs.er.usgs.gov/lizardtech/iser

v/browse?cat=CIR&item=/circ_790.djvu&st

yle=simple/viewdhtml.xsl&wid=750&hei=600&props=img(

Name,Description)&page=0 

Nathenson, M., 1975. “Physical factors determining 

the fraction of stored energy recoverable 

from hydrothermal convection systems and 

conduction-dominated areas”. U.S. 

Geological Survey (USGS) Open File 

Report 75-525. 

Petty, S., Livesay, B.J., Long, W.P., and John Geyer, 

1992. "Supply of Geothermal Power from 

Hydrothermal Sources: A Study of the Cost 

of Power for Future Development." 

Contractor Report Sandia National 

Laboratory, SAND92-7302, November, 

1992. 

Pritchett, J. W., 1998. Modeling post-abandonment 

electrical capacity recovery for a two phase 

geothermal reservoir. Geothermal Resources 

Council Transactions, 22, pp. 521-528. 

Sanyal, S. K., and S. J. Butler, 2005. “An Analysis of 

Power Generation Prospects from Enhanced 

Geothermal Systems.” Geothermal 

Resources Council Transactions, 29. 

Sass, J.H., Priest, S.S., Blanton, A. J., Sackett, P. C., 

Welch, S. L., and M. A. Walters, 1999. 

“Geothermal Industry Temperature Profiles 

from the Great Basin”. U.S. Geological 

Survey (USGS) Open File Report 99-425 

Online Version 1.0. 

http://pubs.usgs.gov/of/1999/of99-

425/webmaps/home.html 

Southern Methodist University (SMU) Geothermal 

Laboratory, 2005. U.S. Regional Heat Flow 

Data Base and Temperature at Depth Maps. 

http://www.smu.edu/geothermal/heatflow/he

atflow.htm 

Tester, J.W., Petty, S., Garnish, J., Batchelor, A., 

Drake, L., and R. Veatch, 2006. “The Future 

of Geothermal Energy: Impact of Enhanced 

Geothermal Systems (EGS) on the United 

States in the 21st Century” Massachusetts 

Institute of Technology (MIT), Cambridge, 

MA. Final Report to the U.S. Department of 

Energy Geothermal Technologies Program. 

Western Governors’ Association (WGA) Clean and 

Diversified Energy Initiative, 2006. 

“Geothermal Task Force Report,” January 

2006. 

http://www.westgov.org/wga/initiatives/cdea

c/Geothermal-full.pdf. 

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Updated U.S. Geothermal Supply Characterization 

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14. ABSTRACT (Maximum 200 Words)

This paper documents the approach taken to characterize and represent an updated assessment of U.S. geothermal 

supply for use in forecasting the penetration of geothermal electrical generation in the National Energy Modeling 

System (NEMS). This work is motivated by several factors: The supply characterization used as the basis of several 

recent U.S. Department of Energy (DOE) forecasts of geothermal capacity is outdated; additional geothermal 

resource assessments have been published; and a new costing tool that incorporates current technology, 

engineering practices, and associated costs has been released. 

15. SUBJECT TERMS

NREL; geothermal; Geothermal Technologies Program; analysis; levelized cost of energy; LCOE; Enhanced 

Geothermal Systems; EGS; Geothermal-Electric Submodule; GES;Geothermal Electric Technology Evaluation 

Model; GETEM; National Energy Modeling System; NEMS; Black Mountain Technology 

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