PROCEEDINGS, Thirty-Eighth Workshop on Geothermal Reservoir Engineering

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

SGP-TR-198

EGS EXPLORATION METHODOLOGY PROJECT USING THE DIXIE VALLEY

GEOTHERMAL SYSTEM, NEVADA, STATUS UPDATE

Joe Iovenitti1, Jon Sainsbury

1, Ileana Tibuleac

2, Robert Karlin

3, Philip Wannamaker

4, Virginia Maris

4, David

Blackwell5, Mahesh Thakur

5, Fletcher H. Ibser

6, Jennifer Lewicki

7, B. Mack. Kennedy

7, Michael Swyer

8

1AltaRock Energy Inc., Sausalito, California 94596, USA

2University of Nevada Reno, Nevada Seismological laboratory, Reno, Nevada 89557, USA

3University of Nevada Reno, Department of Geology, Reno, Nevada 89557, USA

4Univeristy of Utah, Energy and Geoscience Institute, Salt Lake City, Utah 84108, USA

5Southern Methodist University, Dept. of Earth Sciences, Dallas, Texas 75275, USA

6University of California, Berkeley, Department of Statistics, Berkeley, California 94720, USA

7Lawrence

Berkeley National Laboratory, Earth Science Division, Berkeley, California 94720, USA

8AltaRock Energy Inc., Seattle, Washington 98103, USA

jiovenitti@altarockenergy.com

ABSTRACT

Being developed is a calibrated Engineered

Geothermal System (EGS) exploration methodology

using the Basin and Range Dixie Valley Geothermal

Wellfield in Nevada and its surroundings as a

laboratory test site. Previous reports (e.g., Iovenitti;

2012) discussed the assessment of the existing

published (baseline) data with some re-interpretation

of the structure, seismic, gravity, magnetic, and

magnetotelluric data coupled with geostatistical

analysis of the data, and the generation of paired EGS

favorability and trust maps. The favorability maps

were based on the three primary EGS parameters of

interest, temperature, rock type, and stress. The trust

maps essentially reflect the data reliability of the

primary parameters of interest.

Described herein are the (1) geostatistical analysis to

complete the Baseline EGS Conceptual Model, (2)

the new geophysical data collected and interpreted,

(3) new thermal modeling status and results, (4)

results of a reconnaissance soils CO2 flux survey, and

(5) planned stress modeling. To reduce uncertainty

and improve resolution in the geophysical dataset

used in the baseline analysis, we have collected (1)

278 new gravity station measurements, (2) a total of

42 new ambient seismic noise stations under two 21-

station three-month campaigns, and (3) 70 MT

stations. An overview of the new geophysical work

conducted is presented. Details of the gravity-

magnetic modeling, current state of the seismic

interpretation, and the results of the 3D inversion of

the MT data are given by the individual project task

leaders in this workshop.

INTRODUCTION

This paper discusses the ongoing work being

conducted under American Recovery and

Reinvestment Act (ARRA) funding through the U.S.

Department of Energy (DOE) and AltaRock Energy

Inc. to develop a calibrated Engineered Geothermal

System (EGS) exploration methodology, DOE

contract no. DE-EE0002778, using the Dixie Valley

Geothermal Wellfield (DVGW) in central Nevada,

USA as a calibration site (Fig. 1). The Dixie Valley

region was chosen for this investigation due to its

extensive public domain base and geothermal well

data. This project consists of five technical tasks (1)

reviewing and assessing the existing public domain

and other available data; (2) developing and

populating a GIS-database; (3) formulating a baseline

(existing public domain data) geothermal conceptual

model, evaluating geostatistical relationships between

the various data sets, and generating a Baseline EGS

favorability\trust maps from the surface to a 5km

depth focused on identifying EGS drilling targets; (4)

collecting new gravity, seismic, magneto-tellurics

(MT), geologic, and geochemistry data to fill in data

gaps and improve model resolution; and (3) updating

the GIS-database for the newly acquired data and

repeating the components in Task 3 with the

enhanced (new + baseline data).

The work presented here completes an earlier

discussion presented at the Stanford Geothermal

Workshop (Iovenitti et al., 2012) in that it presents

the final elements of the Baseline (existing data) EGS

Conceptual Model. It also discusses the new data

collected Task 4 (described above), an overview of

the gravity and magnetics, seismic and

magnetotellurics (MT) data, and a summary

discussion of other data sets generated. The details of

the current state of the seismic and magnetotellurics

data interpretation are described in other papers in

this workshop and are referenced below.

Figure 1: EGS Exploration Methodology Project Area

(black square) is 50km by 50km. The Dixie

Valley Geothermal Wellfield (DVGW, the

project calibration area), is outlined in red

and covers approximately 170km2. Major

known and inferred faulting is shown in

green. The figure is from Iovenitti et al.

(2012) and after Blackwell et al. (2005).

COMPLETION OF THE BASELINE

STATISTICS

CART Sensitivity Analysis

In Iovenitti et al. (2012-Table 1) we presented a

preliminary Classification and Regression Tree

(CART) Analysis where we examined select input

parameters (explanatory variables) to predict

lithology, temperature (T), and productive

hydrothermal cells (the response variables) using

both cross-section and well data coupled with a

variety of geoscience parameters, and quantify that

prediction. The term cell refers to the gridding

scheme (500m by 500m) used in assessing the

geostatistical significance of the various geoscience

explanatory variables considered.

A summary discussion of CART is provided for

background purposes. Decision trees in CART are

designed to quantify the amount of variability (via r2-

value) in the response variable that can be explained

by the explanatory variables available to make the

prediction. Classification trees predict what category

a response variable falls into whereas regression trees

predict its numerical value. Explanatory variables are

used to determine split points that are chosen to

minimize the number of misclassifications. For more

than two categories (e,g., 2 geologic formations at

Dixie Valley) for any explanatory variable this is

equivalent to choosing the split points so that a

random grid cell has the smallest possible chance of

being misclassified. Thus if we have i categories,

each with probability pi, we minimize the sum of

pi(1-pi), summed over the i categories in each of the

branches at the end of the splits. If the categories are

all perfectly classified, pi will be 1 for exactly one

value of i and then (1-pi) will be 0 for that category.

A regression tree predicts the value of a numerical

response variable. The process of splitting is done to

minimize the squared errors of the predictions when

the predictions are the averages within the subgroups

(e.g., T= 150C<subgroup>150C). If the splitting is

allowed to continue, it will have the undesirable

effect of fitting noise. Because of this, pruning

methods such as cross validation are used to reduce

the tree in a way that presents an accurate realization

of how the predictions should be applied to data not

included in the building of the tree. Amongst the

advantages of CART are that it results in easily

understandable prediction rules and it is free of

underlying assumptions about the data and data error

structure (e.g., MT resistivity model). Drawbacks

include that it is restricted by binary splits and since

the optimization is done from the top down it may

not result in the globally optimal tree.

We recognized in the preliminary analysis that while

high r2-values were determined for the vast majority

of the CART predictions, vertical stress, considered a

surrogate for depth, was the most likely explanatory

variable to split on. Thus, the consideration that this

parameter could produce artificially high r2-value

results for the CART predictions developed. As such,

we conducted a CART sensitivity analysis to

explicitly remove the vertical stress parameter (Table

1). This table displays the range of r2-values resulting

from the CART analyses that explored all

combinations of the seven explanatory variables used

in the analysis for predicting (1) T using cross-

section and well data, (2) lithology type using cross-

section and well data, (3) productive (hydrothermal)

vs. non-productive cells using well data and (4) cells

that are representative of expected EGS conditions

using well data. The r2-value ranges with all

geoscience parameters considered are reported in the

first row for each response variable and shaded white.

Selective CART sensitivity results without the use of

the parameter vertical stress are shown and shaded

green. For example, when predicting T using the well

data, all parameters considered (including vertical

stress) yielded a r2-value of 0.822. When only four of

the seven explanatory variables are considered (3

variables removed from the analysis), the possible

outcomes range from 0.749 - 0.822, while a r2-value

of 0.775 is achieved using P-wave velocity (Vp),

CSC, dilatation and the lithology inferred from the

gravity and magnetics modeling. In all the cases

except when predicting lithology from the cross-

section data, similar values are obtained without

vertical stress. Additionally, when vertical stress was

removed, the parameter Vp was almost exclusively

used in its place with respect to the explanatory a

variable to split on, and consequently, it is considered

critical parameter for CART predictions in these

cases.

EMPIRICAL TEMPERATURE - P-WAVE

VELOCITY RELATIONSHIP

Iovenitti et al. (2012-Figure 8) showed a correlation

plot between T and Vp using measured T in available

project wells and modeled Vp excluding surface Vp

data (at +1km asl) and selected outlier wells with a

low seismic trust value. The empirical relationship

had a r2-value of 0.72 with a confidence interval of

0.54 to 0.83. The confidence interval was calculated

using the bootstrap (re-sampling) method and 95% of

the bootstrap values resulted in a value in interval

indicated.

Table 1: Classification and Regression Tree (CART) Sensitivity Analysis results using cross-section and well

data. The first row for each response variable corresponds to r2-value ranges with vertical stress

considered, while the following rows, highlighted in green, show the r2-values when vertical stress is

removed from the analysis. In most cases with the exception of predicting Lithology using Section Data,

a similar r2 result can be achieved when vertical stress is removed from analysis. The explanatory

variables used in the analyses include (1) temperature, (2) p-wave velocity (Vp), (3) resistivity from MT,

(4) coulomb stress change (CSC) and (5) dilatational strain (dilatation) both from Coulomb Stress

modeling, (6) vertical stress, (7) lithologic formations derived from the geologic assessment and (8)

separately from the gravity-magnetic modeling.

Evaluation of Relationship with respect to Depth

and Lithology

We recognized that both T and Vp are a function of

depth and as such, continued evaluating this

relationship both geologically and geostatistically.

The Vp was plotted with respect to depth per the

major lithologic formations identified in the

geothermal wellfield and coded for T to determine

(1) if the varied lithology, specifically the associated

density (inferred by depth in this case), was a

significant factor in the observed relationship, and (2)

the variation in T per a given depth and lithology.

Data occurring at the surface and outlier wells with

low seismic trust (53-15, 45-14, 66-21, and 76-28)

were removed from the data set to be consistent with

the previous analysis. A general relationship between

Vp and T was observed within the basin-filling

sediments, QTbf, at any given depth and over the

depth range considered (Figure 2), while the

remaining formations had too few data points to

produce meaningful results.

Within the QTbf, the effect of depth on the inferred

T-Vp relationship was examined (Figure 3). A strong

relationship was found only at a depth of 1km (-2km

asl), r2-value of 0.7631. However, when comparing

the T residuals (Tresidual) to Vp, thus removing the

depth component, a negligible r2-value of 0.09 is

found (Figure 4). The Tresiduals can be described as

the actual T minus the predicted T using depth. If

there were a direct relationship between T and Vp,

one would expect that unusually high T at a particular

depth (high Tresiduals) would have higher associated

Vp values, which is not the case as shown in Figure

4. Thus, the effect of depth cannot be separated from

the T and Vp parameters.

Empirical T-Vp Relationship Summary

Tresidual analysis indicates that depth is a

confounding parameter in the empirical T-Vp

relationship. CART analysis, indicates that Vp alone

can predict T with a relatively high r2-value of 0.621

using well data (Table 1). Figure 3 indicates a general

T-Vp relationship independent of depth. Implicit in

the Vp variable, as well as other geoscience

parameters used in the CART analysis, is the effect

of depth. Further investigation of this empirical

relationship is required in other geothermal fields to

determine its viability as a non-invasive tool for

approximating subsurface T-distribution.

Figure 2: Vp-Depth relationship observed within

the basin-filling sediments (QTbf) with

measured temperature at the depth

indicated color-coded by well.

Figure 3: T-P-wave velocity relationship within the

basin-filling sediments (QTbf) at various

depths. R2-values for the various depths

are: 0.0771 at 0.5km, 0.7631 at 1km,

0.4455 at 1.5km, and 0.003 at 2km.

NEW DATA COLLECTED AND CURRENT

STATE OF ITS INTERPRETATION

Individual papers are being presented in this

workshop on the gravity and magnetics, seismic and

MT work. As such, only a brief introduction to this

work is presented here. All the data presented in this

section coupled with baseline geologic sections

(Iovenitti et al., 2012) will be the basis of the

qualitative and quantitative analysis for the Enhanced

EGS Conceptual Model and enhanced exploratory

geostatistics analysis and mapping to be conducted

under Task 5 described in the INTRODUCTION.

Figure 4: Tresiduals and Vp relationship

within the basin-filling sediments.

Depth data is presented at 500m

depth increments relative to sea

level.

Gravity and Magnetics

A total of 278 new gravity measurements were made

in the greater Project Area (Fig. 5) augmenting the

data reported by Smith et al. (2001) and Blackwell et

al. (2005). A total of 14 gravity-magnetics cross-

sections perpendicular to the Stillwater Range trend

and one parallel to it (Fig. 6) were constructed. The

results of this interpretation will be presented in a

later meeting.

Seismic

The core exploration methodology developed in this

project by Dr. Ileana Tibuleac, the Project Seismic

Task Leader, was a new seismic technique which

used complementary information derived from

regional tomographic models of body, shear and

surface waves statistically integrated with shear

velocity models derived from ambient noise to

predict T and rock type. Using the new estimated

seismic models, we will test the supposition that the

regional tomographic models of body, shear and

surface waves statistically integrated with shear

velocity models derived from ambient noise to

predict T and rock type. Using the new estimated

seismic models, we will test the supposition that the

uncertainty and the degree of non-unique seismic

data could be reduced by integration with other

Figure 5: New gravity stations (green circles)

complement the baseline coverage (black

circles) in the grater project area (black

square); see text for gravity data sources).

Geothermal wells are shown as red

circles. The figure is after Blackwell et al.

(2005).

Figure 6: Total aeromagnetic intensity map (in

nanotesla) along with section lines

(Karlin et al., 2013) for the greater

project area.

geophysical and geochemical data into an EGS

conceptual model that will form the basis of an

exploration methodology. The new seismic method

was applied to the project area (Fig. 7) and is referred

to in this seismic section as the DVSA, which is also

known for low seismicity between large seismic

events.

A dense seismic array (21 three-component,

broadband sensors, with an overall array aperture of

45km) was installed in two deployments, each having

a three-month duration (Fig. 7). Ambient seismic

noise and signal rather than active sources were used

to retrieve inter-station and same-station Green's

Functions (GFs), to be used for subsurface imaging.

Another innovative aspect of the seismic work was to

determine if estimating the receiver functions beneath

the stations using noise auto-correlation could be

used to image the substructure.

The objective of the seismic investigation was to

estimate a high resolution (~5km2) P/S seismic

velocity model in the DVSA using new and baseline

information, from independent sources. The seismic

investigation has focused on extracting maximum

information in the calibration area (Fig. 1). To

develop the required data for the DVSA, a larger

region referred to as the Dixie Valley Extended Study

Area (DVESA), Fig. 8, needed to be assessed. The

work is currently on-going, however, the details of

the work-in-progress is presented at this workshop

(Tibuleac et al., 2013).

Magnetotellurics

A total of 70 new tensor MT stations were taken and

merged with 24 baseline (existing) soundings for

providing 94 sites in the greater Project Area (Fig. 9).

These data will be for development of an enhanced

3D MT interpretation for this project.

The resultant 3D resistivity model will be analyzed

together with physical property, structure and state

models arising from potential fields, mapping,

downhole stress and T in an attempt to provide a

calibration of EGS favorability against observables.

Dr. Philip Wannamaker, MT Task Leader, is

presenting the details of his analysis at this workshop

(Wannamaker et al., 2013).

Thermal Modeling

Conductive Model

A 3D conductive model for the project area (Fig.1)

was published by Thakur1 et al. (2012). In summary,

the model is based on the thermal conductivity

contrast between the basin-filling sediments and

range/basement rocks, conductive heat transfer, and a

basement map of the region derived from a 3D

inversion of the gravity data (Fig. 10).

1 Post-doctorate researcher working under Dr. D. Blackwell,

Thermal Task Leader for the project.

Figure 7: Ambient Seismic Noise Station

Locations in the greater Project Area.

Figure 8: Seismic stations in the DVESA (see text

for an explanation) and in the project

area (see Figure 7).

Major variations in heat flow and T in the project

area result from (1) an ~1400m elevation difference

causing topographic effects on the subsurface T and

(2) the geometry of the ~2km thick valley fill causing

the refraction of heat due to the thermal conductivity

difference between the valley fill and range/basement

Figure 9: Baseline and new magnetotellurics

stations in the project area.

Rocks which is of approximately a factor of 100%

Additionally, a pseudogravity transformation of

magnetic data was required to model the possible

effects of the Humboldt mafic igneous complex in

the central and northern part of the Dixie Valley on

the basin shape. Refraction due to the thermal

conductivity contrast and shape of the valley fill

sediments causes a heat flow variation of about 30%

of the 90 ± 30 mWm-2

average regional heat flow.

The heat flow variation is shown along a cross-

section through the Project Area (Fig. 11) as the

Figure 10: A three dimensional representation of

basement depth in the project area as

inferred by a 3D inversion of gravity

data. Elevation is in meters relative to

the sea level. The figure is from and the

caption is after Thakur et al. (2012).

refraction effect is evident along the boundaries and

specifically on the Stillwater Range side of the

DVFZ. The T distribution due to the refraction of

heat flow is quantified as a function of shape of

valley fill geometry. A maximum T of 248oC was

determined at a depth of 5km in Dixie Valley using a

three dimensional conductive steady state model. The

expected conductive T at a depth of 3km (-2km asl)

is shown in Fig. 12. In summary, moderately high

heat flow anomalies along the valley range contact

can be due to the refraction of heat flow. Moderately

high heat flow anomalies along the valley range

contact can be due to refraction of heat flow and may

not be associated with any hydrothermal system.

Figure 11: Heat flow variation along a cross-

section oriented NW-SE due to thermal

conductivity contrasts between

sediments and basement. Figure is from

and caption is after Thakur et al.

(2012).

Figure 12: Temperature slice extracted from the

conductive model at a depth of 3km (-

2km asl). The temperature scale on the

right is in °C. Figure is from and

caption is after Thakur et al. (2012).

3D Pseudo Convective Model

Initial considerations of a 3D convective model

suggested issues with the lack of thermal data within

the Stillwater Range. As a result, an approximation of

the convective field for the calibration area (Fig. 1)

was developed. We refer to this as the 3D pseudo-

convective model.

Using all measured T data in wells available to the

project, the T field was modeled along eight cross-

sections within the wellfield considering the general

hydrothermal model where there are two major

thermal-bearing structures in the DVFZ and a fall-off

in T toward the valley (Blackwell et al. 2005). Cross-

sectional data was gridded within 500m by 500m

cells and applied to the calibration area at various

depths, by interpolating and extrapolating values in

Microsoft EXCEL. This model consisted of three

types of T data (1) measured values, (2) modeled

values along the major cross-sections, and (3)

interpolated and extrapolated values which filled in

missing areas in the calibration area within 1km of

the cross-sectional or well data. The T model

comprised both the convective and conductive

components of the system and is referred to as the

overall T model. The next step was to take the

conductive T field determined by Thakur et al. (2012)

and grid the conductive data in the same manner

described above. By subtracting the expected

conductive T component from the overall T model, a

first approximation of the convective component was

derived using the following equation:

Tconvective ≈ Toverall - Tconductive

We refer to the Tconvective data as the pseudo-

convective component of the system. Figure 13

presents this pseudo-convective model at a depth of

3km along with faults, shallow thermal anomalies,

and the location of active fumaroles. Areas within the

Stillwater Range and to the southwest of the

producing field are not included in the model due to a

lack of data. The model shows that (1) the area within

the DVFZ have elevated T as expected, (2) T fall-off

and approximate the conductive regime both

valleyward towards 62-21 and southwestward

towards 45-14, and (3) the location of shallow

thermal anomalies and fumaroles correlate with areas

that show an elevated convective component. This

pseudo-convective model provides a first estimation

of the accuracy of the conductive model described

above.

3D Convective Model

A 3D convective model had been developed by Drs.

Blackwell and Thakur for the project area. This

model will be presented at later a meeting.

Figure 13: Psuedo-Convective Thermal Model of

the Dixie Valley Calibration Area at a

depth of 3km (-2km asl) Warm colors

indicate a significant convective

component, while cooler colors indicate

a minimal convective component.

Bolded and outline temperature values

represent hard data that was derived

directly from a well measurement. The

location of shallow thermal anomalies

is from Blackwell et al. (2005).

Reconnaissance Soil CO2 Flux Survey

Results of a reconnaissance soil CO2 flux survey

conducted in areas defined as geothermal upflow

zones along the DVFZ (Iovenitti al., 2012) are

presented in Fig. 14. The survey was conducted by

Dr. B. Mack Kennedy, Geochemistry Task Leader

for the project and Dr. J. Lewicki. Soil CO2 fluxes

were measured at 308 locations along seven transects

using a portable West Systems Fluxmeter.

Transects were selected based on structural

intersections along the range-front fault that correlate

with shallow thermal anomalies or the presence of

surface thermal activity (i.e., the unnamed fumarole

area, Fig. 14). Early negative field survey results

precluded a complete assessment of gas flux in the

dilation zones. With the exception of measurements

made near fumaroles and one measurement made in

marshland where soil was visibly wetter and

vegetation denser, fluxes were low (<1-7 g m-2

d-1

;

mean = 2 g m-2

d-1

) and expected for soil CO2

respiration in a semi-arid desert environment.

Anomalously high CO2 fluxes (up to 603 g m-2

d-1

)

were only observed within ~10m of fumaroles within

the unnamed fumarole area. Overall, results show no

surface evidence for geothermal fluid upflow through

fans along measured transects, except that associated

with known fumaroles suggesting that the range-front

fault is relatively impermeable in the near-surface,

except in localized (e.g., fumarole) zones.

Figure 14: Reconnaissance soil CO2 gas flux

survey stations focused on dilatation

and compression zones associated with

the Dixie Valley Fault Zone (Iovenitti et

al., 2012).

Stress Modeling

We expect that given the existing project budget and

time constraints, we can model the fault system

located in the DVGW with Poly 3D, a boundary

element modeling code. This model can provide

quantitative spatial data on how the presence of faults

may concentrates stress and strain locally promoting

fractures favoring fluid flow due to the history of

fault displacement or slip resulting from remote

stresses. This will provide a test of the baseline

conceptual fault model that large scale NE-SW

striking normal faults intersecting older N-S striking

normal faults at Dixie Valley cause stress conditions

that promote either shear or tensile rock failure that is

favorable for geothermal wells and/or EGS (Iovenitti

et al., 2012).

Poly 3D was used to model the Desert Peak

Geothermal field fault system, by making use of very

limited data and performing rigorous sensitivity

studies to construct a fault model for the purpose of

creating maps of local stresses favorable to

geothermal exploration and development (Swyer and

Davatzes, 2013; presented at this workshop). For

example, Fig. 15 shows contoured local minimum

horizontal stress (Shmin) from fault slip at the

reservoir depth. Red areas indicate Shmin has

increased from the remote value, or the regional

stress driving slip, and blue areas are where Shmin

decreases, indicating enhanced tensile fracture

potential. The vectors show the orientation of

potential secondary fracturing from fault slip. The

model determined that increased fracture potential

exists at the location of injection wells, where Shmin

is slightly decreased within the fault relay, and the

production well is located where stresses are

concentrated from a fault junction.

By using the extensive baseline dataset and the newly

available enhanced dataset for Dixie Valley, the Poly

3D modeling code can be improved by using more

data constraints on modeling parameters, provide

insight on the stress conditions resulting from the

postulated fault intersections, and be incorporated in

the favorability and trust criteria for EGS exploration

being developed at Dixie Valley.

Figure 15: Poly 3D results relative to mapped and

modeled fault traces at Desert Peak

Geothermal Field, Nevada along with

locations of injection wells (green), a

production well (red), and an EGS well

(yellow) considered; see text for an

explanation.

CONCLUSIONS

A status update for the development of a calibrated

EGS Exploration Methodology has been presented

along with a number of key findings. We completed

the baseline geostatistics, initially presented in

Iovenitti et al. (2012), by conducting a parameter

sensitivity analysis and also evaluating the effect of

depth (by removing the vertical stress parameter) on

the CART response variable prediction and by further

analyzing the Vp-T relationship. By removing the

parameter vertical stress the various CART analyses

resulted in r2-values which were significant for all

response variable predictions except lithology using

cross-sectional data. The evaluation of the empirical

T-Vp relationship was completed by considering this

relationship with respect to geologic formations, and

by using the Tresiduals. We found that depth was a

confounding parameter implicit in the relationship,

and could be responsible for the high r2-values found

across the various analyses. It is noted that depth is

present in most geoscience parameters. The

relationship between Vp and the Tresiduals show that

there is no relationship using the baseline seismic

data, but this will be further tested to evaluate the

postulation that seismic data could predict

temperature using the higher resolution seismic data

collected for this project.

New geophysical data was collected to improve the

conceptual model and provide an enhanced (baseline

+ new) data set. New gravity measurements and

magnetotelluric data have been integrated with the

baseline data to create detailed geophysical models

that will help resolve the complex structure and

nature of the geothermal system to infer areas

favorable for EGS. A passive seismic array installed

in two deployments will provide a high resolution

seismic data set that will be evaluated to infer if the

such data can be used to predict lithology and/or

temperature. A conductive thermal model was

developed and used to approximately quantify the

convective component within the Calibration Area.

The next steps for the project are to (1) incorporate

the convective thermal model and planned stress

model, (2) integrate the new geophysical models into

a enhanced conceptual model, and (3) create

enhanced EGS Favorability\Trust Maps.

REFERENCES

Blackwell, D. D., Bergman, S., Goff, F. Kennedy, B.

M., McKenna, J. R., Richards, M. C., Smith, R.

P., Waibel, A. F., and Wannamaker, P. E.

(2005), “Description, Synthesis, and

Interpretation of the Thermal Regime, Geology,

Geochemistry and Geophysics of the Dixie

Valley, Nevada Geothermal System,” D. D.

Blackwell and R. P. Smith, ed., unpublished

DRAFT DOE Technical Report, 195 p.

Grauch, V. J. S. (2002), “High-resolution

aeromagnetic survey to image shallow faults,

Dixie Valley geothermal field, Nevada,” U.S.

Geological Survey Open-File Report 02-0384,

http://pubs.usgs.gov/of/2002/ofr-02-0384/

Iovenitti, J., Blackwell, D., Sainsbury, J, Tibuleac, I.,

Waibel, A., Cladouhos, T., Karlin, R., Isaaks, E.,

Clyne, M., Ibser, F. H., Callahan, O., Kennedy,

B. M., Wannamaker, P. (2012), “Towards

Developing a Calibrated EGS Exploration

Methodology Using the Dixie Valley

Geothermal System, Nevada,” 37th

Eighth

Workshop on Geothermal Reservoir Engineering

Stanford University, Stanford, California,

January 30- February 1.

Karlin, R., Iovenitti, J., Sainsbury, J., Blackwell, D.

(2013), “Basin Structure and Faulting in the

Dixie Valley Geothermal Area, Nevada Inferred

from Joint Forward Modeling of Gravity and

Aeromagnetic Data,” 38th

Eighth Workshop on

Geothermal Reservoir Engineering Stanford

University, Stanford, California, February 11-13.

Smith, R.P., Wisian, K.W., Blackwell, D.D. (2001),

“Geologic and Geophysical Evidence for Intra-

basin and Footwall Faulting at Dixie Valley,

Nevada,” GRC Transactions, 25, Aug. 26-69. 8

p.

Swyer, M.W., and Davatzes, N.C., (2013) Evaluating

the Role of the Rhyolite Ridge Fault System in

the Desert Peak Geothermal Field with Robust

Sensitivity Testing through Boundary Element

Modeling and Likelihood Analysis. 38th

Workshop on Geothermal Reservoir Engineering

Stanford University, Stanford, California,

February 11-13.

Tibuleac, I., von Seggern, D., Iovenitti, J., Sainsbury,

J., Biasi, G., Anderson, J. (2013), “EGS

Exploration Methodology Development Using

the Dixie Valley Geothermal District as a

Calibration Site. The Seismic Analysis

Component.” 38th

Eighth Workshop on

Geothermal Reservoir Engineering Stanford

University , Stanford, California, February 11-

13.

Thakur, M., Blackwell, D. D., and Erkan, K. (2012)

“The Regional Thermal Regime in Dixie Valley,

Nevada, USA,” Geothermal Resources Council

Transactions, v. 36, p. 59 – 67.

UTEP PACES GEONET, “Gravity and magnetic

data set repository -Gravity and magnetic data

base of the United States,”

http://irpsrvgis00.utep.edu/repositorywebsite

http://irpsrvgis08.utep.edu/viewers/flex/Gravity

Magnetic

Wannamaker, P., Maris, V., Sainsbury, J, and

Iovenitti, J. (2013), “Intersecting Fault Trends

and Crustal-Scale Fluid pathways Below the

Dixie Valley Geothermal System, Nevada,

Inferred from 3D Magnetotelluric Surveying,”

38th

Eighth Workshop on Geothermal Reservoir

Engineering Stanford University, Stanford,

California, February 11-13.