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Contract No. DE-AC36-08GO28308

Assessment of New Approaches 

in Geothermal Exploration 

Decision Making

Preprint

Sertac Akar and Katherine R. Young

National Renewable Energy Laboratory

Presented at the Fourtieth Workshop on Geothermal Reservoir 

Engineering

Stanford, California

January 26–28, 2015

Conference Paper

NREL/CP-6A20-63546

February 2015
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Assessment of New Approaches in Geothermal Exploration Decision Making

Sertaç Akar, Katherine R. Young

National Renewable Energy Laboratory 15013 Denver West Parkway, Golden, CO 80401

sertac.akar@nrel.gov, katherine.young@nrel.gov

Keywords: Geothermal exploration, decision making, conceptual model, value of information analysis exploration targets.

ABSTRACT

Geothermal exploration projects have a significant amount of risk associated with uncertainties encountered in the discovery of the 

geothermal resource. Two of the largest challenges for increased geothermal deployment are 1) understanding when and how to proceed 

in an exploration program, and 2) when to walk away from a site. Current methodologies for exploration decision-making are

formulated by subjective expert opinion which can be incorrectly biased by expertise (e.g. geochemistry, geophysics), geographic 

location of focus, and the assumed conceptual model. The aim of this project is to develop a methodology for more objective

geothermal exploration decision making at a given location, including go/no-go decision points to help developers and investors decide

when to give up on a location. In this scope, two different approaches are investigated: 1) value of information analysis (VOIA) which is 

used for evaluating and quantifying the value of a data before they are purchased, and 2) enthalpy-based exploration targeting based on

reservoir size, temperature gradient estimates, and internal rate of return (IRR). The first approach, VOIA, aims to identify the value of a 

particular data when making decisions with an uncertain outcome. This approach targets the pre-drilling phase of exploration. These 

estimated VOIs are highly affected by the size of the project and still have a high degree of subjectivity in assignment of probabilities. 

The second approach, exploration targeting, is focused on decision making during the drilling phase. It starts with a basic geothermal 

project definition that includes target and minimum required production capacity and initial budgeting for exploration phases. Then, it 

uses average temperature gradient, reservoir temperature estimates, and production capacity to define targets and go/no-go limits. The 

decision analysis in this approach is based on achieving a minimum IRR at each phase of the project. This second approach was 

determined to be less subjective, since numerical inputs come from the collected data. And it helps to facilitate communication between 

project managers and exploration geologists in making objective go/no-go decisions throughout the different project phases.

1. INTRODUCTION

Exploration of any natural resource has a certain amount of uncertainty and risk (Kacewicz, 2004). Geothermal exploration risk has 

been defined as follows: not achieving an economically acceptable production capacity with minimum flow rates and reservoir 

temperatures (Schulz et al., 2007). While drilling a well at a certain location, a decision-maker is choosing to take that risk with an 

estimated probability of achieving the acceptable production capacity. A decision-maker’s goal is to logically determine where and

when to drill the production well by analyzing data collected at a site.

1.1 Geothermal Exploration Overview

Figure-1 shows a typical exploration program with typical field activities and the decision options typically considered for each step in 

the process. In each phase of exploration, a certain portion of the activities is synthesizes the stratigraphic, structural, hydrologic and 

thermal data. At the end of each phase conceptual models and volumetric assessments of the reservoir are updated to target the 

production wells and total production capacity of the geothermal reservoir. A decision is then made to conduct additional exploration

activities, drill the first production well, or leave the field.

An exploration program typically starts with gathering from literature, existing offset wells and other surface manifestations and then 

continues with applying of various geological, geochemical, and geophysical surveys following successive phases (IGA, IFC, 2013). 

Different exploration programs can be designed with appropriate exploration techniques for different geothermal play types. It is 

important to know that no single exploration technique provides the key to a successful conceptual model and a successful discovery at 

all locations. Conceptual models are typically developed at each stage of exploration as a representation of the current best 

understanding of a geothermal system. A conceptual model is a set of information that best illustrates geology, geochemistry, and 

reservoir fluid and rock properties of a geothermal system (Cumming, 2009). At early exploration stages, the conceptual model can be 

as simple as geological cross-sections correlated with maps of conceptual elements like temperature and some other illustrative data 

sets. At later exploration stages, it can be built-up as complex three-dimensional (3D) subsurface models including 3D geophysical data 

(e.g. magnetotelluric, resistivity) and 3D temperature distribution from temperature data (Akar et al., 2011). Reservoir performance can 

be considered by parameters such as, temperature, pressure, permeability, porosity and water chemistry. Even without wells, isotherms 

can be drawn using geothermometry from surface manifestations, boiling point considerations and characterizations of permeability 

using resistivity and other methods (Cumming, 2009). This information may support the location and the design of temperature gradient 

wells or slim-hole wells. During exploration phases, it has been recommended that more than; one conceptual model could be developed

using the available information, and updated with additional data in each phase (Boseley et al., 2010). Any additional data provides new 

constraints for determining the exact geometry of the reservoir and its controlling structures.
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Figure 1: A Typical geothermal exploration program having different phases, with typical field activities, analyses and the 

decision options.

1.2 Subjectivity in Exploration

In exploration decision making there is subjectivity and uncertainty. We typically talk about uncertainty in terms of quality of data –

how uncertain are the data based on the type of technique employed and the quality of the information? Subjectivity refers to how 

someone's judgment is shaped by personal opinions and feelings instead of outside influences. Subjectivity negatively affects 

reproducibility. Two experts could reproduce the same estimate (e.g., for temperature) with the same level of uncertainty. Both 

uncertainty and subjectivity contribute to project risk. We investigate methods that could be employed to reduce subjectivity and 

increase reproducibility.

Prior to drilling, the character and structure of the subsurface cannot be directly observed, but the exploration techniques described 

above have been developed to help get a sense of what the subsurface might look like prior to drilling. Interpretation of these data 

requires reliance on previous experience and analogs, but is highly subjective to an individual’s background and experience – even 

among highly trained geologists. As an example, in a recent study, 412 different geoscientists (with different backgrounds and 

experience) were asked to interpret a seismic image. The results were surprisingly significant that only 21% of the participants 

interpreted the correct tectonic setting and only 23% highlighted the three main fault strands in the image (Bond et al., 2007). The 

results showed that the differences in interpretations were often related to an individual’s previous experience – in other words, 

they “saw” in the image what they were used to seeing in their previous work.

The further steps of developing geological conceptual models and the assignment of the certainty (reliability) to these models add an 

additional degree of subjectivity. In a recent study, geothermal resource assessment is made based on analogies to the area and power 

density of developed fields (Cumming, 2011). In that study, probability of success in is defined as likelihood function of confidence in 

temperature, permeability, and fluid chemistry. The overall probability of success is calculated by multiplying the probabilities of these 

three components which results a lower probability value and increases the level of subjectivity.

2. METHODOLOGY

In this section, we present the two approaches that we have begun to develop: 1) value of information analysis (VOIA) and 2) 

exploration targeting. Neither method replaces the need for trained geologists and detailed conceptual models. All developers should

determine their own risk tolerance. The two approaches presented here are additional tools that can be used in the decision-making 

process.

2.1 Value of Information Analysis

Many geoscientists request additional data to improve the reliability of decision making but the fundamental question to consider in data 

gathering process is: Is the cost of data-acquisition worth the improvement in decision making (Bratvold et al., 2009)? The VOIA

methodology is designed to determine the impact of purchasing the proposed information against the overall value of the project. It 

considers the reliability of any particular data source and quantifies its value when making a decision with a highly uncertain outcome

(Trainor-Guitton et al. 2014). VOIA uses Bayesian Inference statistics (Sato, 2011) and has been widely used for decision making since 

1960 by many industries - from medicine (Wailoo et al., 2008; Wilson et al., 2010) to petroleum exploration (Ligero et al., 2005;

Bratvold et al., 2009). It has also recently been applied for analyzing the value of magnetotelluric (MT) data in identifying production 

well placement in geothermal exploration (Trainor-Guitton et al., 2013a; Trainor-Guitton et al., 2014).
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We developed the proposed approach in order to broaden the application to a larger suite of exploration activities during the pre-drilling 

phase. The decision to either conduct additional exploration, drill the first production well, or leave the field (Figure 1) is determined by 

evaluating the change in the value of the conceptual model after new information is obtained – relative to the cost of obtaining the 

information.

We started with a geothermal system definition that includes initial lithological, structural, hydrological and thermal parameters in the 

form of one or more conceptual models. Each conceptual model prior to the next decision will have two values to be estimated at this 

point: the reliability of the model and the risk of drilling (cost of an unsuccessful well) without additional exploration (Figure 2). The 

project will have an Expected Monetary Value (EMV) at the initial state. Next, a potential activity (or set of activities) is evaluated, and 

the impact of the resulting data on the reliability of a conceptual model is estimated by assigning an impact value. Impact values can be 

assigned by defining probability distribution functions, giving percentages or based on a scoring scale metric. Then, the reliability of the 

updated conceptual model (based on the data), and the risk (cost of drilling and additional exploration activity) is again estimated. These

estimates come from a geologist or other experts with knowledge of the geologic structure and history (Trainor-Guitton et al., 2013b). 

This results a change in EMV of the project at the following state (Figure 2).

Figure 2: Decision-making with VOIA (The initial conceptual model has a reliability (RM1) and risk value which results an 

EMV. Set of new activities is expected to make an impact on model to reach a higher reliability (RM2) and EMV).

These input data – many of which are highly subjective (e.g., reliability of the two conceptual models and probability that the activity 

can improve the model) – are required in calculating the VOI. To calculate the value of conducting additional exploration, or VOI, the 

Net Present Value (NPV) of the project is estimated with and without the potential activity. The NPV is the sum of cash flow from a 

project devaluated to the present time with the required rate of return for the investment for the project (White et al., 1998).

( ) ∑= + =

n

i i

i

dr

CF NPV 0 1 (1)

where; i is the time of the cash flow which is divided in periods; CFi is the cash flow for period i; and dri is the discount rate for period i.

Next, EMV of the project with and without the potential activity is calculated. In both cases, the EMV is calculated as the NPV times 

the probability of a successful well, or reliability of the model (RM), less the risk (cost of a well), times the probability of an 

unsuccessful well (1 – RM), as shown in Equation 2.

= × ( )- Risk × (1- R M ) EMV NPV RM (2)

Potential actions for at this point are; 1) leave the field (A0), 2) drill the first production well with existing information (A2) or 3) collect 

additional data prior to drilling and then proceed with drilling (A3), as shown in Figure 3. The end product of each decision action is

represented by the EMV of the action. Then, VOI can then be calculated as the difference between the two EMVs, as shown in 

Equations 3 and 4.
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Figure 3: VOIA decision tree: A0, A1, A2 are the decision actions; RM1 and RM2 are the probabilities of confidence in conceptual 

model. Gain and loss are defined NPV and value of risk respectively. Each decision action will result an EMV for the project.

[ ] [ ] VOI = EMV2 − EMV1 (3)

[ ( ) ( )] [ ( ) ( M1 )] VOI = NPV2 × RM 2 -Risk2 × 1-R M2 - NPV1 × RM1 -Risk1 × 1-R (4)

The VOI depends on the degree of uncertainty (prior probability), economic impact of the decision (NPV), and reliability (conditional

probabilities) of the information. The data acquisition is considered to be economic when the VOI is greater than the cost of exploration 

activity. VOI is highly dependent on NPV. In analysis the most likely value for NPV is used for decision making. The value of a 

particular set of information can be calculated to be insufficient or non-economic for a smaller size project but may be economic when 

the NPV of the project is larger. NPV is also limited to the resource potential and controlled by the total size of the geothermal reservoir. 

As discussed in the introduction, there is subjectivity in the development of the conceptual models. There is additional subjectivity in 

assigning the probabilities of risk and success since; 1) there is no standard rubric or methodology for assigning risk, and 2) the people 

who assess the reliability of a conceptual model have their own biases. The level of subjectivity can be reduced in the exploration 

decision-making processes by using historical data statistics collected from analogous geological settings and geothermal play types. 

2.2 Exploration Targeting Analysis

In Exploration Targeting Analysis (ETA) approach, as a first step we started with planning the exploration targets with the available 

data (exploration phase 1), to reach a target internal rate of return (IRR). Then, as a second step, we determined a minimum project IRR

and adjusted the exploration targets to keep the IRR at desired minimum level. In third step, new data was added to the model 

(exploration phase 2, 3, 4) and exploration targets were updated based on these data resulting an updated IRR. In each phase calculated 

IRR was crosschecked with the minimum IRR to give the go/no-go decision for the project (Figure 4). Based on estimated total 

production capacity of the geothermal field, a target production curve was created as a function of reservoir temperature and total flow 

rate. The aim of the model was to keep the production target curve above the go/no-go curve with minimum required IRR of the project

(Figure 5).

Figure 4: ETA decision flow chart (Step 1; Initial targeting, Step 2; Defining Go/No-Go decision points, Step 3; Updating model 

with new data and cross correlating calculated project IRR with minimum IRR)
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Total production capacity of a geothermal area largely depends on the temperature, total volume, and productivity of the reservoir. In 

our model we assign input values for some technical and economic variables. Technical input variables include; desired total installed 

capacity, average target production capacity per well (MWe), minimum average temperature gradient, minimum re-injection 

temperature, and estimated reservoir temperature. Economic input variables include; exploration budget, well testing and development 

budget pump cost, unit power plant cost per MW and effective electricity sales price. Below, we give a brief description of some of the 

principal equations we used in developing the model, and then provide an example of its application at a given location using real (but 

slightly altered) exploration project data.

2.2.1 Defining Targets

Power generation capacity of a geothermal reservoir can be calculated by simple enthalpy equations. Enthalpy of a geothermal 

production well is basically a function of pressure, temperature, and phase of fluid. Production Enthalpy is the difference between the 

enthalpy of the produced fluid and the enthalpy of the dead state assuming the fluid is ambient temperature, ambient pressure, and liquid 

phase (Huenges, 2010). 

Q = m× c × ∆t (5)

where; Q is the total heat flow, m is the total mass flow, c is the specific heat of geothermal fluid and Δt is the system temperature for 

the power plant. Mass flow is the total flow rate of single-phase geothermal fluid. System temperature is the difference between 

production and re-injection temperatures. Specific heat for geothermal fluid is taken as 1 kCal/kg°C in the model. Reservoir temperature 

is estimated by geothermometry calculations using the chemical composition of geothermal fluid, average temperature gradient 

extrapolations, and temperature logging. Flow-line temperature is usually not the same as the reservoir temperature, because there are 

heat losses through the fluid flow within the well bore. Therefore, we added a tolerance percentage to the flow-line temperature estimate 

in the model (i.e., 9% less than the reservoir temperature). The flow rate of a production well cannot be estimated properly before 

drilling the full-size production well. However, there are some methods of estimating the flow rate of full-size production wells from 

flow tests of slim-hole wells (Garg and Combs, 1998). If the reservoir conditions are sufficient, the flow rate of production wells can be 

adjusted to reach the desired capacity with a proper well design, pumping and well enhancement techniques (Sanyal et al. 2007).

Electrical capacity of a geothermal reservoir can be estimated by the volumetric assessment methods and heat-in-place calculations

(Garg and Combs, 2011).

2.2.2 Initial Project Budgeting and Financial Control

Initial project budgeting is required after the initial stage of exploration and includes estimation of exploration cost, drilling cost, 

development cost, power plant cost and operation and maintenance (O&M) cost. In the model, there are some assumptions related to 

project definition such as; project lifetime, project completion time, average annual O&M, and financial contingency as a percentage of 

total capital cost. Initial budgeting is made to estimate NPV and IRR of the project. In this model, the decision making is done based on 

project finances - namely by IRR, which is the nominal discount rate when NPV is equals to zero.

( ) 0 0 1 = + = ∑=

n

i

i

IRR

CF NPV (6)

Go/no-go decision criteria are defined by a team of geoscientists, engineers, economists and managers based on geological, technical 

and financial criteria affecting the IRR of the project. Capital expenditures (CAPEX) components used in the model are exploration 

cost, drilling cost, well pump cost, well testing and reservoir development cost, and power plant electro-mechanic (EM) cost. One of the 

most significant costs within the breakdown of the initial budget is the drilling cost. The cost of drilling increases with depth and the 

average current costs of geothermal wells (in million US dollars) were estimated as a function of reservoir depth by Lukawski et al., 

2014.

_ _ 1.72 10 ( ) (2.3 10 ) 0.62 7 2 3 = × × + × × − − − Geothermal Well Cost Depth Depth (7)

Annual revenue of the project is calculated by simply multiplying installed capacity with annual operation time and effective electricity

sales price. Effective sales price can be estimated by a power purchase agreement (PPA) or feed-in-tariff (FIT). In the model the 

financial component was set as 100% equity financing. However, there is a certain amount of the financial contingency (i.e., 8% of 

CAPEX) to compensate additional costs that may result from debt financing or unexpected drilling risks. Although, there is no detailed 

breakdown of the operational expenditures (OPEX), it mainly consists of staff, maintenance, and pumping cost.

3. ETA EXAMPLE 

To illustrate how the ETA could be used, we present an example. The values presented are modified from a real geothermal power 

plant project. Based on Phase 1 exploration activities and volumetric assessment of the geothermal reservoir, initial target production 

capacity is estimated to be 30 MW. Reservoir temperature is estimated to be 192°C by geothermometer calculations. The minimum 

average temperature gradient is targeted as up to 9°C/100m (Table 1). The re-Injection temperature limit is set as 70°C based on the 

scaling tendency of the geothermal fluid. The average drilling depth for production wells is calculated to be 2133m, to reach the target 

reservoir temperature, assuming average thermal gradient. Average production capacity of a single well is targeted as 4.5 MW, which 

would require at least seven production wells and four re-injection wells to reach the target total production capacity. At the targeted 

temperature, the required minimum total flow rate is calculated to be 562 L/s (at least 80 L/s/well) to reach 30 MW of production 
6

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capacity with 175°C flow line temperature. Flow line temperature is taken 9% less than the reservoir temperature due to heat losses 

through the well bore and production line. Target IRR is calculated to be 17% based the initial cash flow of the project with desired 

parameters. Project completion time and project lifetime are assumed to be 5 years and 25 years respectively. The power purchase 

agreement price is assumed to be 10.5 cents/kWh. The project exploration budget is set as $5 million and the well testing (short term, 

long term & interference) budget is set as $250 thousand/well. No pumping is expected.

Also during Phase 1, the go/no-go decision is determined by setting the minimum IRR value – in this example, the go/no-go IRR is set 

as 13%. Setting this parameter does not dictate all other parameters, but only sets a limit within which all other parameters must fit. In 

our example, the temperature gradient and target reservoir temperature are defined to be 7.5°C/100m and 160°C respectively by the 

geoscientists (Table 1). Re-Injection temperature is estimated to be as low as 70°C to prevent scaling in the system and cooling the 

reservoir. In this case, the required minimum total flow rate is found to be 343 L/s from the enthalpy curve to reach 12 MW of 

production capacity with 145°C flow line temperature. Additional pump cost is added for achieving the minimum required flow rate. 

These inputs are not based on known data at this point; the exercise of developing this go/no-go scenario at this point in the project is to 

understand what a worse-case scenario might look like. Both the initial exploration targets (shown in red, Figure 5) and the go/no-go 

scenario (shown in grey dashed lines, Figure 5) can be plotted on a graph of temperature and flow rate.

After the Phase 1 estimates and go/no-go limits are set, additional exploration activities are conducted and a new set of data is collected

in Phase 2 and Phase 3. With the new data, the target capacity is reduced to 20 MW and all other parameters are modified based on new 

estimations (Table 1). A new curve can be shown on the temperature-flowrate graph (purple curve, Figure 5). In Phase 4, temperature 

gradient and reservoir temperature data are updated from temperature gradient wells and slim-hole wells. The data from Phase 4 

suggests a further reduction in estimated capacity, but the resulting IRR is still above the go/no-go minimum (green curve, Figure 5). 

After drilling the first production well, the total capacity is reduced a third time down to 13 MW, with the IRR just barely above the 

go/no-go limit. Reservoir temperature is measured as 182°C by well logging. Minimum average temperature gradient target is measured 

as 8.5°C/100m (Table 1). Re-Injection Temperature limit is set to be 70°C based on scaling tendency of geothermal fluid. Average 

drilling depth for production wells is calculated to be 2141m. At least four production wells and two reinjection wells are required to 

reach the target total production capacity. Required minimum flow rate is found to be 279 L/s from the enthalpy curve and the wells are

pumped to reach 13 MW of production capacity with 165°C flow line temperature (Table 1). 

Table 1: Project constraints, assumptions for initial budgeting and target production values in different exploration phases

(Yellow cells are the input cells which are updated in each phase based on exploration data; blue cells are the assumption cells 

which are constant and not effected by exploration activities; grey cells has the calculated values based on input variables.)

Exploration Phases

Phase 2 & 3 Phase 4 Drilling

Units Target Go-No/Go Actual Actual Actual

Minimum Desired IRR % 17.0% 13.0% - - -

Calculated IRR % 17.1% 13.1% 16.2% 14.3% 13.7%

Inputs

Installed Power MWe 30 12 20 15 13

Reservoir Temperature °C 192 160 192 180 182

Minimum Average Temperature Gradient °C/100m 9.00 7.50 9.00 8.00 8.50

Minimum Re-Injection Temperature °C 70 70 70 70 70

Average Target Capacity per Well MWe/well 4.5 3.0 4.0 4.0 3.5

Maximum Exploration Budget $ 5,000,000 3,000,000 5,000,000 3,000,000 3,000,000

Assumptions

Project Lifetime years 25 25 25 25 25

Project Completion Time years 5 5 5 5 5

Utilization Factor % 0.95 0.95 0.95 0.95 0.95

Pump cost $/per well 0 500,000 0 0 500,000

Effective Sales Price (PPA or FIT) cents/kWh 10.5 10.5 10.5 10.5 10.5

Power Plant (EM) Cost $/MWe 1,500,000 1,500,000 1,500,000 1,500,000 1,500,000

Power Plant (O&M) Cost $/MWh 9.00 10.00 9.00 9.00 10.00

Financial Contingency % of total capital cost 8% 8% 8% 8% 8%

Conversion Constant kCal/kWh 860 860 860 860 860

Specific Heat kCal/kg°C 1 1 1 1 1

Density kg/m3 998.15 998.15 998.15 998.15 998.15

Production Targets

Number of Production Wells # 7.0 4.0 5.0 4.0 4.0

Number of Re-Injection Wells # 3.0 2.0 2.0 2.0 2.0

Max Drilling Budget per Well $ 4,551,115 4,551,115 4,551,115 5,062,505 4,584,641

Well Testing & Reservoir Development $ 1,750,000 1,000,000 1,250,000 1,000,000 1,000,000

Maximum Total Drilling Budget $ 45,511,154 27,306,692 31,857,808 30,375,027 27,507,846

Target Well Depth m 2,133 2,133 2,133 2,250 2,141

Required Total Flow Rate L/s 562 343 375 332 279

Phase 1
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Figure 5: Reference production curves for different phases and reservoir temperature estimates (Red Curve; Phase 1 target 

production curve for 30 MW capacity, Grey Dashed Curve; 12 MW capacity production curve for go/no-go limit, Purple Curve; 

20MW capacity production curve for phase 2&3, Green Curve; 15 MW capacity production curve for phase 4, Blue curve; 

actual production curve for 13 MW capacity after drilling first production wells, Red Dashed Line; 192°C reservoir temperature 

estimate for phase 1,2&3, Green Dashed Line; 180°C reservoir temperature estimate for phase 4, Blue Dashed Line; 182°C 

Reservoir temperature measurement during drilling phase, Blue Dotted Line; 165°C Flow line temperature measurement during 

drilling phase, Grey Dashed Line; 160°C go/no-go reservoir temperature limit) 165°C Flow line temperature was not sufficient to 

support 13 MW production capacity with the free flow rates and the wells were pumped to reach 13MW capacity and stay 

above the go/no-go curve.

4. RESULTS 

In the scope of this study, two approaches were investigated to develop a best-practices methodology for objective geothermal 

exploration decision making at a given location, including go-no go decision points to help developers and investors in deciding when to 

give up on a location. Among those two approaches ETA is found to be less subjective than value of information analysis.

A sensitivity analysis was conducted to determine the variation in the results of IRR due to changes in five key input factors: 

temperature gradient, reservoir temperature, total capacity, sales price of electricity, and exploration cost (Figure 6).

The results of sensitivity analysis showed that: 

• temperature gradient, reservoir temperature and electricity sale price have the largest effect on IRR 

• exploration cost has a very minor effect on IRR

• drilling cost has a major effect on project IRR which is greater than the effect of electricity sales price (increasing reservoir 

temperature while keeping temperature gradient stable increases drilling cost and reduces IRR) 

• total capacity estimate has a smaller effect on IRR than reservoir temperature and thermal gradient and its sensitivity curve 

makes a shift at positive 10% deviation from baseline. This is because of expected average capacity per well (an additional 

well is needed to achieve greater capacity which increases the total drilling cost and reduces IRR). 

One other important observation is the direct correlation between reservoir temperature and average temperate gradient in calculating 

the project IRR. As an example, consider that, a project having 13MWe total capacity with 227°C reservoir temperature and 

10.6°C/100m temperature gradient has 13.7% IRR. It requires a total flow rate of 160 L/s from four wells which produces at least a 40 

L/s flow rate per well. Another project having the same total capacity with 136°C reservoir temperature and 6.4°C/100m temperature 

gradient has a the same IRR (13.7%) value. It requires a total flow rate of 640 L/s from four wells which produces at least a 160 L/s 

flow rate per well. In terms of project economics, both projects are equivalent to each other but the project with lower reservoir 

temperature and thermal gradient requires four times more flow rate per well. If the required flow rates are impractical to achieve from 

production wells— in terms of both reservoir parameters and well design— then the project may not be economically feasible. On the 
8 

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contrary, if higher flow rates can be achieved by additional pumping with proper casing design and reasonable reservoir drawdown, then 

the project may still be economically feasible. 

Figure 6: Sensitivity analysis for the 13 MW production capacity geothermal power plant project at drilling phase

High costs of data acquisition (i.e., MT. seismic, temperature gradient wells, slim-hole wells...etc.) at a specific phase of exploration 

may be misleading and lead developers to make incorrect decisions for the project. However, sensitivity analysis showed that 

exploration budget has a minor effect in changing IRR of the project. Thus, the impact of the activity for exploration targeting is more 

important than its cost. Drilling cost has more influence on IRR of the project than exploration cost. So it is important to estimate the 

productivity of the wells. Geothermal wells with low productivity can be enhanced with pumping, hydraulic fracturing, perforating or 

acidizing. This may add additional cost to the project but if the model supports the minimum required IRR value, the project may still be 

economic. The only variable in the sensitivity analysis, which cannot be controlled by exploration activities, is the effective sales price. 

The results showed that it has almost the same impact as thermal gradient on IRR. So, it can be said that, projects having higher sales 

prices (PPA or FIT) will have higher IRR.

5. DISCUSSION

The analysis of two different decision-making approaches is a first order analysis, and could benefit from additional work and 

modifications to make them more accurate and impactful. This initial work shows that VOIA has a high degree of subjectivity in 

assigning probabilities. The level of subjectivity can be decreased by defining probability distribution functions, assigning impact 

buckets or reliability percentages, which may allow for increased reproducibility by multiple trained geologists making independent 

assessments. The second approach is determined to be less subjective because numerical inputs come from collected data. These, too, 

have an element of subjectivity, however, as each geologist may interpret data differently, as indicated. Exploration targets and go/no-go 

limits are calculated from these input parameters, and estimates for geological constraints are updated in every exploration phase. 

This initial work shows that VOIA has a high degree of subjectivity in assigning probabilities. The level of subjectivity can be decreased 

by using historical data statistics collected from analogous geological settings and geothermal play types, or creating a scoring scale 

metric for specific set of activities. One outcome of the VOIA approach is the effect of project capacity on the calculated value of the 

data. Consequently, a set of exploration activities may have higher VOI for larger capacity projects while they are not found to be 

economic to purchase for smaller size projects. 

ETA approach is appears to be less subjective and more reproducible because numerical inputs come from collected data but it still has 

significant uncertainty. The input variables used in the ETA model is not always a definite numbers and they may have a range of 

values. In this case Monte Carlo Simulation can be used to reduce the uncertainties in the input variables. In the ETA model, energy 

conversion calculations are based on an average enthalpy value. It may be different in a double-phase flow having different brine-gas 

ratio (enthalpy of steam is much greater than enthalpy of brine). Brine gas ratio is controlled by flashing point within wellbore and can 

also be adjusted by flow line pressure through a steam separator or placing a submersible pump below the flashing point of the 

production well.
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The model is developed excludes the drilling risk and assumes 100% equity financing. However, a certain percentage of financial 

contingency is added to the model to compensate for additional costs from unexpected drilling risks and debt financing. The Geothermal

Electricity Technology Evaluation Model (GETEM) and the System Advisor Model (SAM) tools can be used for a better financial 

estimate (Blair et al., 2014).

Typically, companies explore several areas at the same time in the early stages of exploration, narrowing down their number of 

prospects at each phase, allowing them to target the top areas in their portfolio prior to drilling. Although the examples outline above 

are shown for a single area, these two approaches could be used in this multi-area approach to exploration.

6. CONCLUSIONS

Our effort in making these analyses is to develop a methodology for more objective geothermal exploration decision making at a given 

location to help developers and investors. Geothermal exploration projects have a significant amount of risk associated with 

uncertainties encountered in the discovery of a new resource. Before staring an exploration project, developers should determine their

own risk tolerance. Two important challenges for project deployment are 1) understanding when and how to proceed in an exploration 

program, and 2) when to walk away from a site. In this scope, we tried to analyze two different approaches and concluded that, the 

VOIA and exploration targeting methods can be used as complementary studies in decision making for evaluating a new geothermal 

resource. It may be a better approach to start with VOIA approach in early stages by checking parameters and results of ETA approach 

because exploration targeting method is so dependent on flow rates and it is not practical to estimate flow rates in early stages. If 

calculations show unrealistic flow rates with very confident temperature estimates, the developer may decide to discontinue exploration. 

NPV of the project may also be estimated using ETA approach and that value may be used in the probabilistic calculations of VOIA. In 

later stages the decision making may then switch to exploration targeting to make decisions on drillings and implementation of the 

project. Thus, complementary use of VOIA and ETA with control of IRR can be a useful decisions making tool in geothermal 

exploration projects. This may also help to facilitate communication between project managers and exploration geologists in making 

objective go/no-go decisions throughout the different project phases.

ACKNOWLEDGEMENTS

This work was supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), Geothermal

Technologies Office (GTO) under Contract No. DE-AC36-08-GO28308 with the National Renewable Energy Laboratory (NREL). The 

authors wish to thank reviewers for their comments and suggestions including Chad Augustine, Emily Newes, Margaret Mann, and 

David Mooney. In addition, we also wish to thank Kendra Palmer (NREL) for her technical review of the paper. All errors and 

omissions are the responsibility of the authors.

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