Conference Paper

NREL/ CP-6A2-47388

February 2010

Report on the U.S. DOE 

Geothermal Technologies 

Program’s 2009 Risk Analysis

Katherine R. Young and Chad Augustine

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

REPORT ON THE U.S. DOE GEOTHERMAL TECHNOLOGIES PROGRAM’S

2009 RISK ANALYSIS

Katherine R. Young

National Renewable Energy Laboratory 

1617 Cole Blvd.

Golden, CO, 80401-3305, United States

e-mail: Katherine.Young@nrel.gov

Chad Augustine

National Renewable Energy Laboratory 

1617 Cole Blvd.

Golden, CO, 80401-3305, United States

e-mail: Chad.Augustine@nrel.gov

Arlene Anderson

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

Geothermal Technologies Program (GTP) 

1000 Independence Ave SW

Washington, DC, 20585, United States

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

The U.S. Department of Energy (DOE) Geothermal 

Technologies Program (GTP or “the Program”) 

conducted a detailed risk analysis of their annual

research, development, and demonstration (RD&D) 

portfolio. The Program worked with the National 

Renewable Energy Laboratory (NREL) to 

implement a probabilistic risk analysis of the GTPsponsored RD&D, primarily enhanced geothermal 

systems (EGS) in accordance with Program budget 

authority. EGS technologies are in the early stages of 

development, and GTP-sponsored, multi-year 

demonstration projects are now underway to 

demonstrate technical feasibility, reduce risk for 

industry, and improve EGS best practices. The risk 

analysis examined estimates of improvement 

potential for two metric types: EGS-enabling 

technologies potential and EGS cost-improvement 

potential. NREL also evaluated potential 

improvements in hydrothermal exploration. The 

analysis employed a spreadsheet add-in that uses 

Monte Carlo simulation in conjunction with the 

Geothermal Electric Technology Evaluation Model

(GETEM). Four risk groups (exploration, 

wells/pumps/tools, reservoir engineering, and power 

conversion) comprised of industry experts, national

laboratory researchers, academic researchers and

laboratory subcontractors estimated the RD&D 

impacts using probability distributions for three 

budget levels and two future time frames. Risk results 

were expressed in terms of each metric’s units and 

input into GETEM to estimate impacts on levelized 

costs of electricity. The resulting detailed risk 

analysis summarizes the industry’s current thinking 

on various metrics and potential for research 

improvement. Although the well drilling/construction 

and plant capital costs are key targets for cost 

reduction, all experts believed (1) that RD&D needs 

to occur first in enabling technologies for EGS and 

(2) that Program RD&D funding should not all be 

spent in only a few areas.

ABSTRACT

DOE has standardized the annual risk process for all 

programs managed by its Office of Energy Efficiency 

and Renewable Energy (EERE). The DOE 

Geothermal Technologies Program tasked NREL 

with conducting its annual risk analysis, which DOE 

uses to:

BACKGROUND

1. Meet the National Academy of Science’s 

requirement to report uncertainty

2. Improve project, program, and portfolio design, 

performance, and likelihood of success
2

3. Clarify issues associated with accepting, 

managing, or rejecting risks

4. Link science research opportunities with applied 

energy RD&D

5. Increase decision-maker understanding of 

potential RD&D results

6. Obtain answers to key RD&D questions.

Additionally, the Program uses the risk information 

to set technical goals and to provide input for the 

supply curve used in estimating benefits under the 

Government Performance and Results Act (GPRA).

The task goal and principal product were a 

probabilistic risk analysis of GTP-sponsored RD&D 

primarily for enhanced geothermal systems (EGS). 

Addressing ubiquitous sources of EGS, beyond the 

more easily accessible resources, was mandated by 

Program appropriators when the Program was 

restarted in Fiscal Year 2008. EGS technologies are 

in the early stages of development and GTPsponsored, multi-year demonstration projects are now 

underway (1) to demonstrate technical feasibility and 

reduce risk for industry and (2) to better understand 

and improve EGS best practices. 

The risk analysis approach taken examines estimates 

of improvement potential derived from program 

RD&D work for two types of technology 

performance metrics (TPMs): EGS-enabling 

technologies

METHODS

1 potential and EGS cost-improvement 

potential. Additionally, potential improvements in 

hydrothermal exploration were also evaluated. Risk 

results are expressed in terms of each metric’s units, 

levelized costs of electricity (LCOE)

2

, or both.

Specifically, the analysis used @Risk, a spreadsheet 

add-in that uses Monte Carlo simulation, to drive the 

Geothermal Electric Technology Evaluation Model 

(GETEM), a techno-economic systems analysis tool 

for evaluating and comparing geothermal project 

cases. By itself, GETEM is a deterministic model; it 

computes LCOE values for a set of user-specified 

input variables that address almost 50 project criteria. 

 1 Risk analysts applied the term “enabling technology” to 

metrics (such as pump temperature capability) that were not 

used as input to GETEM but for which potential 

improvement values would interest the Program.

2 The LCOE, also known as a “busbar cost,” is a present 

value of a producer’s cost of electricity for commercial and 

industrial power systems. It covers the exploration, 

development, construction, and operating phases of a 

project. LCOE accounts for time-dependent values of 

equity, borrowed capital, operation and maintenance costs, 

and discounted values of other cash-flow terms such as 

taxes, insurance, and escalation.

A particular geothermal project reference scenario

was defined by allocating a profile of values to the 

input variables. GETEM’s function is to examine 

“improved technology” cases compared with the 

reference scenario by quantifying potential benefits 

of research in terms of improvements to the baseline 

input variables defined in the reference scenario. The 

@Risk/GETEM risk model evaluates multiple ranges 

of potential impacts of RD&D, coupled with 

corresponding levels of probability of the occurrence 

of those impacts. The evaluation computes 

probability distributions of LCOE for geothermal 

power projects.

Four risk analysis groups—one each for exploration, 

wells/pumps/tools, reservoir engineering, and power 

conversion—provided probability distribution

estimates of potential improvement from Program

RD&D investments for 21 TPMs. These groups were 

comprised of 32 experts from industry, federal 

laboratories and agencies, and academia. 

The experts, analysts and GTP personnel worked 

together to develop a reference scenario plant. The 

goal was to create a scenario that was reasonable to 

develop, and could be deployed in a wide range of 

geographic locations. The resulting scenario 

parameters are shown in Table 1. 

Each expert group provided present-day values for 

each of their group’s metrics based on expert 

discussion of multiple reports, publications, and data 

sources. These present-day values were used as 

baseline input in GETEM against which expert 

improvement probability distribution estimates were 

compared.

Experts then independently provided input for each 

metric in the form of quantitative probability 

distributions and qualitative comments. The 

individual expert probability distributions for each 

metric were aggregated into a single distribution 

using Monte Carlo sampling in @Risk.

The results of the risk assessment are (1) aggregated 

expert input distributions and summaries of experts’ 

comments for each metric and (2) for the cost 

metrics, projected impacts on EGS project LCOE. 

The mean values of the aggregated expert 

distributions for each metric are given in Table 2.

Full results are provided in the NREL Technical 

Report.

RESULTS

The results, including both the qualitative comments 

and the quantitative potential for improvements, were 

thorough and cohesive in three of the four expert 
3

groups: exploration, wells/pumps/tools, and power 

conversion. (See the Conclusions section for a 

discussion of results for the fourth expert group, the 

reservoir engineering group.) Table 3 summarizes the 

effect of Program RD&D investment on individual 

TPM improvements from these three groups. Table 4

gives the same information for hydrothermal 

exploration.

Table 1. Reference Scenario Parametersa

Parameter Value Parameter Value

Year of the $ Dec-08 Water Loss/Total Injected 0.02

Geothermal Type EGS Thermal Drawdown (fluid) 0.3%/yr

Resource Rock Temperature 225o C 437o F Geofluid Pump Efficiency 0.6

Fluid Temp at Power Plant 

Inlet 200o C 392o F Flasked Wireline Tool Service Time

10 hours

Ambient Temperature 15o C 59o F Permanent Tool Lifetime 6 years

Exploration few to none O&G wells in area Pump Lifetime (then replace) 3 years

Easy Drilling 

(e.g., Sed overburden) 1,500 m 4,922 ft Pump Depth Setting 1 km 3,281 ft

Resource Rock Type igneous Total Dynamic Head (TDH) 1.2 km 4,000 ft

Drilling Coat Curve (in 

GETEM) median cost curve Injection Pumping none/low to prevent water losses downhole

Resource Stress Regime normal faulting transitional to 

strike-slip Number of Fractured Intervals 2

Well Depth 6 km 19,686 ft Pump horsepower 1065 HP

Well Deviation from Vertical 0 degrees Gross Capacity 30 MWe

Well Casing ID at TD 17.78 cm 7 in Net Capacity 20 MWe

Deviated Ramp Length (at 

45°) 500 m 1,641 ft Capacity Factor 0.95

Well Separation 650 m 2,133 ft Energy Conversion binary

Producer-Injector Well Ratio 2:1 Cooling Technology air-cooled

Producer Flow Rate 

(per well) 60 kg/s Plant Lifetime 30 years

Injection Temperature 80o C 176o F

a The risk experts defined a set of EGS parameters to define the reference scenarios. Parameters included GETEM inputs, as well as other 

qualitative parameters (e.g. resource stress regime) to allow the experts to give risk feedback using common base assumptions.
4

Table 2: Mean values of aggregated expert input.a

 

Varied Metric (TPM) 2009 2015 2025

# ANNUAL FUNDING LEVEL: Units --- $0 $30M $60M $0 $30M $60M

E1 Non-Well Exploration Costs (EGS) $million 1.41 1.14 1.06 0.97 1.10 0.94 0.82

E1 Non-Well Exploration Costs (Hydro) $million 1.22 1.18 1.13 1.07 1.16 1.06 0.90

E2 Exploration Well Success Rate (EGS) % 64 64 66 68 66 69 73

E2 Exploration Well Success Rate (Hydro) % 35 37 41 43 40 45 49

W1 Well Drilling/Construction Cost $million 22.3 21.6 20.3 19.0 20.6 18.3 16.6

W2 Production Pump Cost (per well) $million 1.5 1.5 1.3 1.2 1.4 1.2 1.0

W3 Downhole Pump Temperature °C 167 208 230 245 224 251 269

W4 Pump Horsepower HP 320 433 671 843 529 865 1,074

W5 Wireline Tool Temperature °C 175 194 220 237 209 248 272

W6 Permanent Equipment Temperature °C 125 151 179 199 165 211 237

W7 Zonal Isolation Differential Pressure Psi 0 134 158 173 148 183 203

W8 Zonal Isolation Temperature °C 152 171 193 210 194 220 260

R1 Well Stimulation Cost per well tripletb $million 8.4 7.9 7.5 6.8 7.3 6.4 5.8

R2 Reservoir Creation Probabilityb % 59 59 55 57 58 56 63

R3 Short-Circuit Mitigation Probabilityb % 45 44 44 47 44 46 50

R4 Thermal Drawdown Rateb %/yr 13.2 13.3 13.0 12.3 12.6 12.1 11.1

R5 Production Well Flow Rateb kg/s 35 35 37 41 36 42 51

R6 Producer-Injector Ratiob ratio 2.9 2.7 2.7 2.8 2.7 2.9 3.3

P1 Binary System Capital Cost $/kW 2,500 2,470 2,380 2,010 2,390 2,250 1,870

P2 Binary System O&M Cost/Yr ¢/kWh 2.2 2.2 2.1 1.9 2.2 2.1 1.8

P3 Brine Effectiveness W-h/ lbm 9.5 9.5 9.7 10.0 9.6 10.0 10.4

a Mean values of aggregated expert probability distribution functions for technology performance metrics (TPMs) in risk assessment b Expert input for the reservoir engineering group were not thoroughly vetted, and consequently, not cohesive.

Table 3: Summary of 50th Percentile LCOE a,b

Varied Metric (TPM)

Total Potential LCOE for EGS Reference Scenario

2015 2025

ANNUAL FUNDING LEVEL: No DOE

Funding

DOE 

Planned

DOE 

Expanded

No DOE

Funding

DOE 

Planned

DOE 

Expanded

Well Drilling/Construction Costs 

25.3

24.3 23.3

23.9

22.2 21.0

Plant Capital Costs 25.2 24.5 23.6 23.0

Well Stimulation Costs 25.3 25.1 23.7 23.5

Plant O&M Costs 25.3 25.1 23.8 23.6

Pump Costs 25.3 25.3 23.8 23.8

Exploration Success Rate 25.3 25.3 23.9 23.8

Non-Well Exploration Costs 25.3 25.3 23.9 23.9

a Values for 50th percentile LCOE (in Year 2008 ¢/kWh) for EGS reference scenario for single TPM improvements under no budget, target 

budget ($30 million), and over-target budget ($60 million) levels b For comparison: Current estimate of LCOE = 26.4 ¢/kWh. LCOE calculated for reference scenario binary EGS plant. Binary EGS plant 

reference scenario assumptions: reservoir temperature = 225°C, reservoir depth = 6,000 m, power plant design temperature = 200°C. EGS 

“enabling technologies” assumed constant: production well flow rate = 60 kg/s, thermal drawdown rate = 0.3%/year, and producer-injector ratio 

= 2:1. For aggregated expert TPM values, see Table 2.
5

Table 4: Summary of 50th percentile LCOE—Hydrothermal Reference Case c

Varied Metric (TPM)

Total Potential LCOE for EGS Reference Scenario

2015 2025

ANNUAL FUNDING LEVEL: No DOE

Funding

DOE 

Planned

DOE 

Expanded

No DOE

Funding

DOE 

Planned

DOE 

Expanded

Exploration Success Rate 

12.5

12.5 12.5

12.2

12.1 12.1

Non-Well Exploration Costs 12.5 12.5 12.2 12.2

c Values for 50th percentile LCOE (in Year 2008 ¢/kWh) for reference hydrothermal plant for single TPM improvements under no budget, target 

budget ($30 million) and over-target budget ($60 million) levels. Current estimate of LCOE is 12.8 ¢/kWh. LCOE calculated for reference 

scenario hydrothermal EGS plant (reservoir temperature = 175°C, reservoir depth = 1,524 m, power plant design temperature =175°C, production 

well flow rate = 44.2 kg/s, thermal drawdown rate of 0.3%/year, and producer-injector ratio of 3:1).

The calculated results give GTP management a 

picture of the likelihood of advancing EGS 

technologies and reducing EGS levelized costs. The 

results will also help the Program set future target 

metrics.

Expert input for the reservoir engineering group were 

not thoroughly vetted, and consequently, not 

cohesive. Therefore, these input were not used, and 

instead Program goals were assumed for reservoir 

engineering enabling technologies such as production 

well flow rate and thermal drawdown. If these goals 

are achieved, the results of analysis of the remaining 

risked metrics indicate that reducing well 

drilling/construction costs and power plant costs 

show the greatest potential for reduction in LCOE for 

EGS.

The recent rise in drilling costs is partly responsible 

for the large role drilling costs play in overall EGS 

economics. At the time of the risk elicitation with 

experts, drilling costs were near historic highs 

because of high rig rental rents caused by high crude 

oil and natural gas prices (which led to increased 

demand for oil and gas drilling) and the scarcity of 

steel and cement.

The drilling costs used by the experts in this analysis 

reflect these high costs and represent drilling costs at 

a point in time based on market conditions. Drilling 

costs have subsequently decreased significantly from 

these highs. The decreases in future drilling costs 

from RD&D and the learning-by-doing projected by 

the experts indicate cost reductions relative to only 

the assumed drilling costs and do not consider market 

volatility. Although the recent decline in drilling 

costs may lessen the role drilling costs play in overall 

EGS power costs, the lessons learned from the risk 

assessment exercise still apply—decreases in drilling 

costs from expanded RD&D will significantly affect 

EGS power costs.

Although the well drilling/construction and plant 

capital costs are key targets for cost reduction, all 

experts believed (1) that RD&D needs to occur first 

in enabling technologies for EGS and (2) that 

Program RD&D funding should not all be spent in 

only a few areas. The industry has the potential to 

benefit by investment in all four areas: exploration, 

wells/pumps/tools, reservoir engineering, and power 

conversion technologies. Expert comments on 

potential improvement in each of these four areas are 

summarized in the NREL technical report.

While reservoir engineering parameters were 

considered enabling technologies and their values 

fixed during the risk assessment, improvements in 

reservoir engineering have significant potential to 

decrease EGS LCOE. Overall project well costs can 

be lowered by decreasing thermal drawdown rates 

and increasing flow rates, which both decrease the 

number of wells that are needed. The cost reduction 

potential shown in Table 5 indicates significant 

potential for EGS project cost reduction from 

improvements in reservoir engineering. These 

improvements may come at a cost (see Conclusions), 

and trade-off studies should be conducted to better 

understand the interdependence among TPMs. 

Table 5: Effect of thermal drawdown rate and 

production well flow rate on 50th percentile LCOE 

(in Year 2008 ¢/kWh) values for reference EGS plant 

(assuming producer-injector ratio of 2:1).

Prod. Well Flow Rate: 30

kg/s

60

kg/s

90

kg/s

Thermal Drawdown LCOE (¢/kWh)

3.0%/yr 75.8 44.5 38.0

1.0%/yr 49.1 30.2 26.6

0.3%/yr 42.4 26.4 23.4

For example, increases in well flow rates can reduce 

the number of wells needed for a geothermal project, 

thereby potentially reducing cost. However, these 

increases in flow rate come at a cost—increased 

friction loss requiring increased pumping energy

needs. Increasing the well diameter can help to 
6 

mitigate these issues, but this then increases well 

costs. 

RD&D investment to reduce well costs will lower the 

LCOE, but more important, RD&D investment in 

reservoir engineering and plant performance can 

significantly reduce the number of wells needed. This 

and other similar relationships need to be better 

understood and designed for to improve EGS project 

design and minimize geothermal costs. 

DISCUSSION

A distribution of electricity costs based on the input 

TPM distributions was calculated in GETEM using 

the @Risk add-in for Microsoft Excel. For each 

scenario considered, a Monte Carlo simulation 

consisting of 1,000 iterations was performed. 

Preliminary simulations showed that use of all the 

expert aggregated TPM distributions resulted in EGS 

LCOE distributions in which the majority of the 

iterations involved reservoir characteristics that were 

not economically feasible assuming current costs for 

the reference EGS plant as shown in Figure 1. 

0

500

1000150020002500

Probability Density

Figure 1: Distribution of LCOEs for reference 

scenario EGS plant assuming current costs as 

provided by experts for all TPMs in 

Table 2. 

The wide distribution and large tail are caused by 

wide probability distributions for two individual 

metrics: thermal drawdown rates and production well 

flow rates. The upper portions of these probability 

distributions (high thermal drawdown rates, low 

production well flow rates) both causes high number 

of required wells over the lifetime of the plant. The 

effect of these TPM distributions for even modest 

ranges of production well flow rate and thermal 

drawdown is shown in Table 5. For these reasons, the 

Program chose key TPMs as enabling technologies 

and set them to the Program goal values. Specifically, 

fixed reference scenario values were assumed for (1) 

the thermal drawdown rate (0.3 %/year), (2) the 

production well flow rate (60 kg/s), and (3) the 

producer-injector ratio (2:1). 

Figure 2 shows the LCOE distribution for the EGS 

reference scenario assuming current costs when these 

three enabling-technologies TPMs are fixed at the 

above-stated goal values and the expert aggregated 

distributions are used for the remaining cost metrics. 

Figure 2: Distribution of LCOEs for reference EGS 

plant assuming current costs and aggregated expert 

distributions in Table 2 with EGS enablingtechnologies TPMs fixed at constant values of 

Production Well Flow Rate of 60 kg/s, Thermal 

Drawdown Rate of 0.3%/year, and Producer-Injector 

Ratio of 2:1 

The importance of the production well flow rate and 

thermal drawdown rate on LCOE for the EGS 

reference case can be seen by examining Table 5. A 

thermal drawdown rate of 3.0%/year requires redrilling of the EGS reservoir four times over the 30-

year lifetime of the power plant, whereas a 1.0%/year 

drawdown rate requires the reservoir to be re-drilled 

only once, and a 0.3%/year drawdown rate does not 

require re-drilling. 

A similar trend is seen for increasing the production 

well flow rate. The reference scenario assumes wells 

with bottom-hole diameters of 7.0 inches. As the 

flow rate in the production (and injection) wells 

increase, friction losses in the wellbores and reservoir 

rise so that more power is required to run the 

injection and downhole production pumps, partially 
7

offsetting the decrease in LCOE costs from the lower 

number of wells required at higher production well 

flow rates. The same effect was observed for 

GETEM runs for the conditions shown in Table 5

using a 3:1 producer-injector well ratio. The pressure 

losses in the injection well for these runs were so 

large that the LCOE was actually higher for 60-kg/s 

and 90-kg/s producer well flow rate cases than when 

a 2:1 producer/injector well ratio was used. 

Increasing the injection well and production well 

diameters would eliminate the friction losses in the 

wellbore at higher flow rates, but these wells would 

also be more expensive. Input on well costs as a 

function of bottom-hole diameter was not gathered 

from the experts, so the reference plant scenario 

could not be optimized as a function of varying 

production well flow rates. However, such 

information would be useful to gather in future tradeoff analyses and risk assessments.

According to the experts’ experience, fewer large 

wells are generally less expensive than more small 

wells for a given total flow rate. If reservoirs can 

sustain high flow rates, larger wells will be drilled.

The results show that the greatest potential for 

reduction in levelized cost of EGS power is in 

reduction in well drilling/construction costs, followed 

by reduction in power plant costs. Although these 

two areas would be key targets for cost reduction, all 

experts believed that (1) RD&D needs to first occur 

in enabling technologies for EGS and (2) RD&D 

funding should not all be spent in only a few areas.

Tables 3 and 4 consider only the effect of RD&D

improvements from each TPM in isolation, and they 

report only the median LCOE for each scenario, 

which more or less corresponds to GETEM results 

when the median value from the expert distribution is 

used. The data in these tables ignore the effect of 

advances in multiple TPM areas and does not address 

the LCOE’s possible use of TPM values from the full 

range of the aggregated expert distributions.

Figure 3 shows possible combinations of drilling 

costs, power plant costs, and stimulation costs that 

result in an LCOE of 22.5 cents/kWh for the EGS 

reference case. The figure was made by fixing the 

drilling and stimulation costs as given percentages of 

their mean value from the aggregated expert 

distributions and solving for the power plant cost that 

resulted in an LCOE of 22.5 cents/kWh. (For 

example, when the drilling and stimulation costs are 

100% of their mean values, the power plant costs 

must be about 11% of its mean value to result in an 

LCOE of 22.5 cents/kWh for the EGS reference 

case). The slope of the curves shows the relative 

importance of drilling costs to power plant costs to 

overall project LCOE; the steep slope indicates that 

drilling costs factor more heavily in determining the 

LCOE than do power plant costs. The spacing of the 

lines for the range of stimulation costs considered 

gives their relative importance; closely spaced lines 

indicate that stimulation costs do not heavily 

influence the overall LCOE. The dotted red lines 

indicate the mean and 10th percentile values of the 

drilling and power plant costs from the aggregated 

expert distributions given in Table 2, so that the red 

box indicates the range of reasonable values for the 

drilling and power plant costs. A much wider range 

of possible drilling costs than power plant costs was 

considered by the experts. The figure indicates that 

significantly lower LCOE costs are possible for the 

EGS reference case than indicated in Table 3 if 

drilling costs, power plant costs, or both can be

lowered by amounts considered feasible by at least 

some of the experts.
8

0 2 4 6 8 10 12 14 16 18 20 22 

0 

500 

1000 

1500 

2000 

2500 

3000 

0%

20%

40%

60%

80%

100%

120%

0% 20% 40% 60% 80% 100%

Drilling Costs (M$/well)

Plant Capital Costs ($/kW)

Plant Capital Costs (% of Mean Value)

Drilling Costs (% of Mean Value)

100% Stimulation Costs

75% Stimulation Costs

50% Stimulation Costs

25% Stimulation Costs

10%ile Value

10%ile Value

EGS Reference Case

22.5 cents/kWh LCOE

Mean Value

Mean Value

Assumes "Goal" EGS Reservoir

• 0.3%/yr Thermal Drawdown

• 60 kg/s Production Well Flowrate

• 2:1 Injector Producer Ratio

Figure 3. Drilling, power plant and stimulation cost scenarios that give 22.5 cents/kWh EGS reference case LCOE. 

Axes show plant and capital costs as both actual dollar values and as percentage of mean value from aggregated 

expert distributions. Dotted red lines indicate 100% of mean value and 10th percentile values from aggregated 

expert distributions.

The detailed risk analysis, which summarizes the 

industry’s current thinking on various metrics and 

potential for research improvement, made 

considerable strides in establishing a risk analysis 

protocol to be used by the Geothermal Technologies 

Program on a regular basis. The following risk tools

can be used, with minimal updates, in future risk 

assessments:

CONCLUSIONS

• EGS reference scenario

• Expert briefs

• Risk schedule

• Risk presentations

• Expert input worksheets

The results of the risk analysis indicate that the 

greatest potential for reduction in levelized cost of 

EGS power is in reducing well drilling/construction 

costs and power plant costs. The near-historic high 

drilling costs (due to high rig rental rents and the 

scarcity of steel and cement), which was used by 

experts in this analysis, is partly responsible for the 

large role it plays in overall EGS economics. Since 

the risk assessment exercise, drilling costs have 

decreased significantly. However, the decreases in 

future drilling costs from RD&D and learning-bydoing projected by the experts in this analysis

indicate cost reductions relative to only the assumed 

drilling costs and do not consider market volatility. 

The lessons learned from the risk assessment exercise 

still apply, and decreases in drilling costs from 

expanded RD&D will significantly impact EGS 

power costs.

Although this risk study identified well 

drilling/construction and power plant costs as key 

targets for cost reduction, all experts believed that (1) 

RD&D needs to first occur in enabling technologies 

for EGS and (2) RD&D funding should not all be 

spent in only a few areas. The industry has the 

potential to benefit by investment in all four areas: 

exploration, wells/pumps/tools, reservoir 

engineering, and power conversion technologies.

Additionally, improvement in reservoir engineering 

has significant potential to decrease well costs by 

decreasing thermal drawdown rates and increasing 

flow rates, both of which decrease the number of 

wells that are needed. The cost reduction potential, 

shown in Table 5 above, indicates that trade-off 

studies should be conducted to improve on the 

reference scenario design. For example, increase in 

well flow rates can reduce the number of wells 

needed for a geothermal project, thereby potentially 

reducing cost. But, increases in flow rates come at a 

cost—increased friction loss and increased pumping 

needs. Increasing the well diameter can mitigate 
9

these issues, but this increases well costs. This and 

other similar relationships need to be better 

understood and designed for to minimize geothermal 

costs. RD&D investment to reduce well costs will 

lower the LCOE, but more importantly, RD&D 

investment in reservoir engineering and plant 

performance can significantly reduce the number of 

wells needed.

The results of the risk assessment—both the 

qualitative comments and the quantitative potential 

for improvements—will provide the Program with 

guidance in developing Program targets and focusing 

RD&D efforts to obtain these targets. These 

comments and potential improvements were thorough 

and cohesive in three of the four expert groups: 

exploration, wells/pumps/tools, and power 

conversion.

Reservoir engineering expert discussions throughout 

the risk process, though lengthy, were never 

concluded. Consequently, the results were 

inconsistent and conflicting, were deemed invalid,

and were not used. Comments from the reservoir 

engineering experts reveal that further discussion is 

needed in future risk elicitation activities to validate 

and better understand the reservoir engineering 

feedback on potential for metric improvements.

For future analyses, experts should be contacted 

earlier and more reservoir engineering experts should 

be targeted to obtain a stronger response from this 

group. It may also be useful to schedule meetings for 

risk experts at times that do not coincide with other 

major geothermal industry events to avoid conflicting 

meetings.

RECOMMENDATIONS

Not all of the risk information could be covered in 

the time allotted for risk expert meetings. If the same 

group of experts is used for future risk elicitation 

analyses, the same time allotment may be sufficient. 

Planning for additional time, however, particularly 

for the reservoir engineering expert group, may be 

helpful.

In this risk assessment, cost improvements had to be 

linearly extrapolated from 2025 (the last year for 

which data were gathered from the experts) to 2050 

for purposes of modeling in market penetration 

models. In future risk analysis, the time frame should 

be extended to 2050 to reflect the time frames in the 

market penetration models SEDS (Stochastic Energy 

Deployment System) and MARKAL (Market

Allocation).

Program personnel, NREL’s risk analysts, and the 

risk experts understand that the EGS scenario defined 

for this study may not be the best design for an EGS 

system. Because system design and trade-off analyses 

were beyond the scope of this risk assessment, this 

scenario was used. As system design is better

understood and results from trade-off analyses 

currently funded by the Program become available, 

the system design and reference scenario will 

continue to be updated and improved. Additionally, 

RD&D projects may help redefine the EGS system in 

the future. Future risk assessments can rely on these 

newly developed system design parameters as they 

are developed.

Future risk assessments should focus on reservoir 

engineering, since technology understanding will 

change as current RD&D activities progress.

Future edits of the Program’s Multi-Year Research 

Development and Demonstration Plan (MYRD&D)

should reflect recommendations by the experts in all 

metric areas addressed in this risk assessment, both 

for the current state of EGS technologies and for 

potential for improvement. Qualitative comments 

made by the experts should also be considered in 

MYRD&D planning efforts.

Finally, as the Program expands, consideration 

should be given in future risk analyses to expand the 

scope to include other resources, such as 

geopressured reservoirs and co-production from oil 

and gas wells.
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1. REPORT DATE (DD-MM-YYYY)

March 2010

2. REPORT TYPE

Conference Paper

3. DATES COVERED (From - To)

4. TITLE AND SUBTITLE

Report on the U.S. DOE Geothermal Technologies Program's 2009 

Risk Analysis

5a. CONTRACT NUMBER

DE-AC36-08-GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

K. R. Young and C. Augustine (NREL); A. Anderson (U.S. DOE)

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NREL/CP-6A2-47388

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GT08.3001

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National Renewable Energy Laboratory

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13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

NREL conducted an annual program risk analysis on behalf of the U.S. Department of Energy Geothermal Technologies 

Program (GTP). NREL implemented a probabilistic risk analysis of GTP-sponsored research, development, and demonstration 

(RD&D) work, primarily for enhanced geothermal systems (EGS). The analysis examined estimates of improvement potential 

derived from program RD&D work for two types of technology performance metric (TPM): EGS-enabling technologies potential 

and EGS cost improvement potential. Four risk teams (exploration, wells/pumps/tools, reservoir engineering, and power 

conversion) comprised of industry experts, DOE laboratory researchers, academic researchers, and laboratory subcontractors 

estimated the RD&D impacts and TPM-improvement probability distributions. The assessment employed a risk analysis 

spreadsheet add-in that uses Monte Carlo simulation to drive the Geothermal Electric Technology Evaluation Model (GETEM). 

The GETEM-based risk analysis used baseline data from the experts’ discussion of multiple reports and data sources. Risk 

results are expressed in terms of each metric’s units and/or the program’s top-level metric: levelized costs of electricity (LCOE). 

Results—both qualitative comments and quantitative improvement potential—are thorough and cohesive in three of the four 

expert groups. This conference paper summarizes the industry’s current thinking on various metrics and potential for research 

improvement in geothermal technologies.

15. SUBJECT TERMS

DOE; Geothermal Technologies Program; GTP; enhanced geothermal systems; EGS; program risk analysis; probabilistic risk 

analysis; RD&D; estimated RD&D impacts; improvement potential; cost improvement; probability distributions; technology 

performance metric; TPM; exploration; wells; pumps; geothermal tools, reservoir engineering, power conversion; Monte Carlo 

simulation; Geothermal Electric Technology Evaluation Model; GETEM; levelized costs of electricity; LCOE

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