PROCEEDINGS, Thirty-Eighth Workshop on Geothermal Reservoir Engineering

Stanford University, Stanford, California, February 11-13, 2013

SGP-TR-198

ESTIMATED POWER GENERATION COSTS FOR EGS

Greg Mines1 and Jay Nathwani

2

1Idaho National Laboratory;

2US Department of Energy,

e-mail: 1Gregory.Mines@inl.gov;

2Jay.Nathwani@ee.doe.gov

ABSTRACT

The Department of Energy‟s (DOE) Geothermal

Technologies Office has developed a model that

provides representative cost and performance

estimates for the generation of power from

geothermal resources. This model, Geothermal

Electricity Technology Evaluation Model (GETEM),

was originally developed to evaluate the generation

costs from hydrothermal resources and to provide a

means of assessing how technology improvements

could impact those costs. Recent modifications to the

model have focused on incorporating the costs

associated with developing geothermal resources that

utilize EGS technologies for power production. This

paper provides an overview of both those changes to

GETEM, and the EGS resource scenarios that the

DOE is using in its assessment of the impact of

technology on future generation costs. The basis for

the EGS specific scenarios is discussed along with a

summary of the estimated costs and levelized-cost-

of-electricity (LCOE) for the EGS scenarios being

considered.

BACKGROUND

In the mid 2000‟s the DOE Geothermal Technologies

Office (GTO) developed GETEM to provide both a

method of conforming to GPRA (Government

Progress and Results Act), and a tool that could both

identify major contributors to generation costs and

provide a method of assessing how technology could

impact those costs (Entingh, 2006). The focus of

early versions of the model was on the technologies

and costs associated with conventional hydrothermal

resources. The model‟s estimates were to be

„representative‟ of the costs that one would encounter

in developing a defined hydrothermal resource

scenario using either an air-cooled binary or flash-

steam technology for the power plant. Subsequent

work model has focused on better characterizing the

costs and performance of each of the project phases,

with an emphasis on EGS resources.

In 2011, feedback was received from the geothermal

industry that GTO‟s estimates of generation costs for

new geothermal developments were considerably

lower than those the industry was encountering. One

area where the estimates were considered to be low

was for those activities associated with both

exploration and confirming that a resource has been

found that is commercially viable. A team comprised

of GTO, national lab, and contractor personnel was

subsequently formed to make a concerted effort to

improve the model‟s characterization of cost and

performance for all project development phases for

both undiscovered hydrothermal and EGS resources.

The resulting changes that have been made to the

model have incorporated input received from both

interviews with industry and solicitations for industry

comment on cost and performance estimates

produced by the model.

MODEL UPDATES

The focus of the efforts by the GTO analysis team

has been on updating the model to:

Show the impact of the variability in reservoir

conditions (temperature, flow/permeability and

depth) on the generation cost;

Reflect the risk associated with early project

phases by including a methodology that

accommodates both down-selection to the final

site selected for commercial development and

exploration/confirmation failures at non-selected

sites,

Assess the impact of the uncertainty associated

with the model‟s cost and performance estimates

for the different phases and elements of the

project development;

Identify areas where the model‟s characterization

of cost and/or performance need improvement,

and where possible making those improvements;

Estimate generation costs using a methodology

that is consistent with other DOE EERE

programs; this methodology replicates a

discounted cash flow analysis accounting for

both the time required and the discount rate for

each phase of the project.

In order to show how the variations in the resource

conditions can impact the generation costs, five

scenarios were established for EGS. These scenarios

were derived internally within the GTO, taking into

account different factors including the EGS resource

potential (Augustine, 2011) and geographic diversity,

as well as consistency with other GTO programmatic

activities. They are intended to be representative of

the range of resources conditions that might be

considered for new EGS developments, and not the

near-field expansion of hydrothermal resources. The

conditions for the scenarios developed are shown in

Table 1 (the highlighted Scenario C is discussed in

more detail in this paper). The selection of the values

used for flow rate, ratio of production to injection

wells and plant size are based in part on

conversations with industry, as well as discussions

within the GTO, with the assumption that they could

be achieved using current technology at the nth

EGS

project.

The effect of risk associated with the early phases of

a project is now accounted for by allowing duration

and discount rate (cost of money) to be varied for

each phase, as well as allowing for failures during the

exploration and confirmation phase. The values used

in the Reference Cases that define the five EGS

scenarios are shown in Table 2. As indicated, a

higher discount rate is applied to the exploration and

confirmation phases than is applied to the well field

development phase; and a higher rate is applied to the

well field development phase than to the power plant

construction. A model user can vary these rates, as

well as the time required to complete each phase.

To reflect the probability that not all potential

resources considered will result in a successful

development, the model now allows for multiple

exploration sites to be considered before any drilling

is done. Some or all of the sites considered will have

subsequent drilling activities, with one of these sites

ultimately resulting in commercial power production.

The costs for all exploration work and subsequent

drilling activities, including those sites that are not

used for commercial development, are included in the

model‟s estimate of power generation costs. These

changes to the model were primarily intended for

evaluation of undiscovered hydrothermal scenarios;

however the same methodology was used for EGS. In

assessing the LCOE for EGS resources, the GTO

assumed that for every 4 sites evaluated, drilling

would occur at all 4 sites, and 3 of those sites would

result in commercial projects.

To assess how the different assumptions and

calculated values used impact the generation costs, a

sensitivity analysis was performed for ~60 variables

of the ~185 GETEM inputs. For each variable

selected for the analysis, a LCOE was determined

using a most likely or reference value, which

represented a hypothesis of what could be done at an

nth

project using current technology. Calculations

were then repeated for each variable using both a

conservative value and an optimistic value for the

selected variable. This analysis identified which

variables significantly impacted the LCOE, and

provided the GTO with an indication of the relative

importance of the different elements of a project‟s

phases on LCOE. It also assists in identifying where

technology improvements would contribute to

reaching LCOE target goals.

The sensitivity analysis reinforced that LCOE‟s are

sensitive to the drilling costs for the wells, which lead

to an effort to improve the model‟s characterization

of those costs. Prior to this effort, drilling costs were

derived from cost curves that were generated using

data from 2004 (Mansure, 2005), with those 2004

costs brought forward in time using a US Bureau of

Labor Statistics Producer Price Index (PPI) for oil

and gas wells. To improve the model‟s projections of

drilling costs, a series of well cost estimates were

generated by Sandia National Laboratory and

provided to the GTO team. These costs were used to

Table 1. EGS Resource Scenarios

Scenario

Case

Temperature

(°C) Depth (km)

Well Flow

Rate (kg/sec) Wells

Production:Injection Power Plant

Type Sales (MW)

A 100 2 40 2:1 Air Cooled

Binary 10

B 150 2.5 40 2:1 Air Cooled

Binary 15

C 175 3 40 2:1 Air Cooled

Binary 20

D 250 3.5 40 2:1 Flash 25

E 325 4 40 2:1 Flash 30

Table 2. EGS Project Phase Duration and Discount Rates

Phase Duration

Scenarios A,B & C Scenarios D & E

Discount

Rate

Permitting- Exploration & Confirmation 1 year 1 year 30%

Exploration 1 year 1 year 30%

Confirmation 1.5 year 1.5 year 30%

Utilization Permit – Field & Plant 1 year 1year 15%

Well Field Development/Completion 2 year 2 year 15%

Power Plant Construction 2 year 1.5 year 7%

Total Duration Pre-Operation Activities 5.5 year 5.5 year

Operations 20 year 20 year 7%

generate new cost curves that were recently

incorporated into the model.

The model was also modified to incorporate a

methodology for estimating power generation costs

that will tentatively be implemented in all of the

DOE‟s EERE programs. The EERE methodology

replicates a discounted cash flow analysis. It allows

both the discount rate and duration of different

project phases (including operation) to be varied, and

includes both a depreciation schedule and taxes.

Previously a Fixed Charge Rate (FCR) was used to

estimate the LCOE: this was consistent with the

approach used in the EIA Annual Energy Outlook

Report. While the model can utilize either

methodology for calculating the LCOE, the GTO

now uses the EERE methodology in its analysis

activities. The model also includes a separate, simple

discounted cash flow sheet, though those LCOE

estimates are not „reported‟ in the model output.

Other changes that were incorporated include

providing input needed to estimate the effects of

permitting, leasing, and taxes and insurance on

LCOE. Previously with the FCR approach the model

assumed a fixed plant/project life of 30 years; the

new EERE methodology allows project life to be

varied up to 40 years.

EGS SCENARIO COSTS

In addition to defining the temperature, depth, well

flow rate and power sales, a set of model inputs were

defined to establish a Reference Case LCOE for each

of the EGS scenarios. To the extent possible, the

input values used were based upon the interviews

performed with industry. Table 3 summarizes some

of the more important input parameters used to define

the EGS C scenario. Note that for all the EGS

scenarios evaluated, it is assumed that only the

injection wells are stimulated.

Table 3. Selected EGS C Scenario Model Inputs

Variable Reference Value Used Variable Reference Value Used

Resource EGS Reservoir

Temperature 175°C Well Flow 40 kg/s

Depth 3 km Temperature Drawdown 0.5% per year

Exploration Hydraulic Drawdown/Buildup 0.4 psi per 1,000 lb/hr

# of Exploration Sites Evaluated Prior to Drilling 1.33 (Productivity/Injectivity Index) 4.6 kg/s per bar

Cost per Site Evaluated $500K Subsurface Water Loss 5% of injection flow

# of Sites Drilled 1.33 Makeup Water Cost $2,000 acre/ft

Site Exploration Drilling Costs $3,000K per site Power Plant

Confirmation Conversion System air-cooled binary

# of Sites with Confirmation Drilling 1.33 Transmission Line Cost $0

Success Rate 100% Binary Plant Performance (brine effectiveness) produces LCOE minimum

# of Successful Wells Required 3 Economic Parameters

Cost per Well $9,363K Power Sales 20 MW

Stimulation Cost $2,500K Project Life 20 year

Well Field Development Contingency (applied to all capital expenditures) 15%

Success Rate 100% Royalties BLM schedule

Well Cost $7,802K Depreciation MACRS - 5 yr

Ratio Production to Injection Wells 2

Stimulation Cost per Injection Well $2,500K

Surface Equipment Cost $200K per well

Utilization Permit (Well Field and Plant) $1,000K

Table 4. EGS Scenario Estimated Costs

EGS Results Scenario A Scenario B Scenario C Scenario D Scenario E

Resource Temperature 100°C 150°C 175°C 250°C 325°C

Resource Depth 2 km 2.5 km 3 km 3.5 km 4 km

Plant type Air-Cooled BinaryAir-Cooled BinaryAir-Cooled Binary Flash Steam Flash Steam

# of Production Wells 21.5 7.6 7.9 6.4 4.3

Ratio of Production to Injection Wells 2:1 2:1 2:1 2:1 2:1

Production Well Cost - each $5,187K $6,965K $8,973K $8,237K $10,280K

Injection Well Cost - each $5,187K $6,965K $8,973K $11,210K $13,678K

Total Geothermal Flow 860 kg/s 303 kg/s 316 kg/s 256 kg/s 171 kg/s

Power Sales 10 MW 15 MW 20 MW 25 MW 30 MW

Geothermal Pumping Power 3,499 kW 738 kW 383 kW 997 kW 679 kW

Plant Output 13.50 MW 15.74 MW 20.38 MW 26 MW 30.68 MW

Generator Output 17.07 MW 20.34 MW 24.4 MW 27.42 MW 31.72 MW

Power Plant Cost $8,128/kW $4,668/kW $3,597/kW $2,091/kW $1,571/kW

Overnight Project Capital Cost (with contingency) $343,960K $187,291K $217,994K $176,620K $152,299K

Present Value of Project Capital Cost $396,252K $235,706K $276,042K $229,634K $211,177K

Exploration & Confirmation (₵ /kW-hr) 9.44 7.27 6.56 4.83 4.88

Well Field Completion - Including Stimulation (₵ /kW-hr) 32.46 7.47 7.24 4.56 2.53

Permitting (₵ /kW-hr) 0.37 0.23 0.17 0.13 0.11

Power Plant (₵ /kW-hr) 16.98 7.13 5.30 3.09 2.33

O&M (₵ /kW-hr) 17.22 5.65 4.74 4.78 3.53

Levelized Cost of Electricity - LCOE (₵ /kW-hr) 76.47 27.75 24.01 17.4 13.39

As shown in Table 4, there is considerable variation

in the generation cost estimates for these five EGS

scenarios. Not surprisingly the higher temperature

resource had lower LCOE even though its well costs

were 2 to 2-1/2 times that of the cooler, shallower

resources. While it is assumed that power sales

increase with the fluid temperature, the primary

reasons for the lower generation costs at the elevated

temperatures are the increased energy content of

these fluids. This is illustrated by the reduced number

of production wells needed to produce the indicated

level of power sales. It should be pointed out that for

all the scenarios the costs for the exploration and

confirmation phases are effectively the same, and that

successful confirmation wells are used to support

plant operation during the operational phase. As a

result, for those scenarios requiring fewer production

and injection wells, a larger fraction of the capital

costs associated with finding and creating the

reservoir and well field is attributed to exploration

and confirmation.

Note also that for these scenarios, the annual O&M

cost estimates are in part determined as being some

fixed fraction of the capital costs for the project.

Hence the O&M contribution to LCOE at the low

temperature scenario is high due to both the higher

annual cost associated with its higher project capital

costs and the lower power sales. For the higher

temperature scenarios it was assumed that a flash-

steam conversion system would be used. The model

estimates the amount of water that would be needed

for makeup to an evaporative heat rejection system,

and includes the cost of that makeup water in the

O&M contribution to the LCOE.

EGS SCENARIO COSTS

Because there a few EGS projects upon which to base

the different input variables that are used to define

the EGS scenarios, there is considerable uncertainty

associated with those inputs specific to EGS. The

sensitivity analysis that was performed identified

those having the greatest impact on the LCOE‟s. The

following discussion is specific to those parameters

that had the larger impacts on generation costs for

EGS Scenario C. This discussion focuses on those

variables that are specific to the EGS resources, and

does not address the uncertainty in the inputs

impacting the conversion system and pump costs and

performance.

Though perhaps not specific to the EGS resource,

two variables that impacted the LCOE estimates were

the level of power sales and the project life that was

used. For all scenarios, the period of operations was

20 years and for Scenario C the power sales was 20

MW. The impacts of different power sales and

project life on the LCOE for this scenario are shown

in Figure 1. While the model projects a continued

benefit from increasing plant size or longer project

life, the magnitude of this benefit diminishes with as

either parameter is increased.

Of the variables that are more specific to the

subsurface, drilling costs and production well flow

rate have large impacts on the LCOE. While there is

uncertainty as to the costs for stimulation (the model

assumes only the injection wells are stimulated), the

sensitivity analysis suggests variation in the cost for

Figure 1. Effect of Project Life and Power Sales on

the LCOE for Scenario C

stimulation has a relatively small impact on the

LCOE for the range over the range of costs

considered. Figure 2 shows the impact of variation in

the well cost and the stimulation cost on the LCOE.

Clearly the well drilling cost has a significant impact

on the LCOE. The interviews with industry indicated

that there is considerable variation in this cost.

Because of its importance, the GTO will continue its

efforts to gather data on these costs and update the

model as appropriate. Again there is little data

available on stimulation costs. Changing the

reference value stimulation cost of $2.5M per

injection well by +50% impacted the LCOE, but not

as much as the assumption that only the injection

wells are stimulated. If the production wells were

Figure 2. Effect of Well Drilling and Stimulation

Costs on LOCE for Scenario C

also stimulated, the estimated LCOE would increase

as indicated by the red square symbol in Figure 2.

Production well flow rate has a significant impact on

the LCOE. The number of production wells needed to

produce a given power sales decreases as the flow per

well increases; this reduces the cost to develop the

well field and reservoir. There are negative

consequences to increasing the production well flow

rate. The geothermal pumping power is a direct

function of both the hydraulic drawdown (inverse of

the Productivity Index) and the well flow rate. For a

given hydraulic drawdown, as the well flow rate is

increased, the required geothermal pumping power

increases. In order to maintain the same level of

power sales, a larger power plant and increased total

geothermal flow (i.e., wells) are required. In Figure 3

Figure 3. Effect of Reservoir Hydraulic Performance on LCOE for Scenario C

the effect of the values used for well flow rate and

hydraulic drawdown or Productivity/Injectivity Index

on the LCOE are shown. These estimates indicate

that for a given reservoir hydraulic performance there

is a flow rate that would produce a minimum LCOE,

and as expected as the reservoir Productivity/

Injectivity Index increase, the flow rate at which this

LCOE minimum occurs also increases.

The productivity of a geothermal resource is

dependent upon the well flow rate, temperature

decline with time, and productivity/injectivity of the

reservoir. In the case of hydrothermal resources, the

relationships between these parameters are inherent

to the reservoir. With EGS resources, these

relationships will depend upon the characteristics of

the reservoir that is created. Because the reservoir is

„engineered‟, it can be postulated that it will be

possible to create a reservoir that could maximize the

amount of heat extracted from a given reservoir

volume – at a cost. Hence for EGS, the cost to create

the reservoir becomes another parameter to be

included in the tradeoff between flow rate and both

thermal and hydraulic drawdown in optimizing the

LCOE for a given EGS scenario.

While GETEM can account for the effects of the

relationship between the well flow rate and the

hydraulic drawdown, as shown in Figure 3, it has no

methodology to relate the changes in the thermal

drawdown with flow rate. Nor does it have a

methodology of relating the cost and size of a

reservoir that is created to either the hydraulic or

thermal drawdown. It must rely on the model user to

provide input for these different parameters that

correctly depicts the consequences of increasing well

flow rate or decreasing either the hydraulic or

thermal drawdown.

FURTHER MODEL DEVELOPMENT

At present the LCOE analysis team is reviewing the

information obtained during the most recent

interviews with industry. This review will determine

whether inputs that define the EGS scenarios require

further revision, as well as whether further changes

are needed for the methodologies used to

characterize cost or performance. An effort is in

progress to determine whether a simplified well cost

method can be integrated into the model to allow the

GTO to assess how technologies specific to well

drilling can impact the generation costs. Once these

efforts are completed, an updated version of the

model will be available from the DOE GTO web site.

Further model development work will consider some

of GETEM‟s limitations. At present the model only

utilizes air-cooled binary or flash steam conversion

systems; other conversion system types may be

considered (dry steam, water-cooled binary, hybrid

flash/binary), as well as direct use or combined

power/direct use applications. Another area where

additional work may be done is updating the

properties of water that are used in order to improve

model estimates for high temperature resources

(~300°C). In the near term, there will be

consideration as to how to simplify the model‟s input

to make it use less arduous.

The GTO will continue to collect data and

information needed to validate and improve the

model‟s estimates for power generation costs. The

GTO continues to solicit comments relative to the

utility of the model, the validity of its methodology

and estimates, and any suggestions for improvements

or changes.

ACKNOWLEDGEMENT

The authors wish to acknowledge the other members

of the GTO Analysis team. Those members include

Chad Augustine (National Energy Renewable

Laboratory), Mark Paster (Consultant), and Ella

Thodall, Erin Camp and Steven Hanson (DOE

service contractors). We would also acknowledge

the technical support received from both Seungwook

Ma (DOE) in the integration of the EERE

methodology into GETEM and John Finger

(Consultant) in providing the well cost estimates used

to update the model‟s drilling cost projections. The

authors and other team members also would like to

thank those individuals from the geothermal industry

who provided the time and patience to participate in

the team‟s interviews.

This work was supported by the U.S. Department of

Energy, Assistant Secretary for Energy Efficiency

and Renewable Energy (EERE), under DOE-NE

Idaho Operations Office Contract DE AC07

05ID14517.

REFERENCES

Augustine, C. (2011), “Updated US Geothermal

Supply Characterization and Representation for

Market Penetration Model Input”, NREL/TP-

6A20-47459, 18-23, 32-36

Entingh, D. J. and Mines, G. L. (2006), “A

Framework for Evaluating Research to Improve

U.S. Geothermal Power Systems”, Geothermal

Resources Council Transactions, v. 30, 741-746.

Mansure, A. J., Bauer, S. J., and Livesay, B. J. (2005)

Geothermal Well Cost Analyses 2005,

Geothermal Resources Council Transactions, v.

29, p 515-519.