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
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.
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(2005), “Description, Synthesis, and
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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/
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Eighth
Workshop on Geothermal Reservoir Engineering
Stanford University, Stanford, California,
January 30- February 1.
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(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,
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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)
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Nevada, USA,” Geothermal Resources Council
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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.