University of Nevada, Reno
An AVO and seismic attribute analysis of the
Southern San Emidio Geothermal System,
Northwestern Nevada
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Geology
by
Joseph Dierkhising
Dr. John Louie / Thesis Advisor
Dr. Graham Kent / Thesis Advisor
May, 2015
Abstract
Geothermal fields in the Basin and Range province are often found in highly
faulted and fractured subsurface zones that provide the necessary permeability for
vertical fluid flow. Frequently, subsurface structure and petrophysical properties vary
substantially from what is predicted from surficial mapping alone. Advanced seismic
reflection techniques, including amplitude versus offset (AVO) and seismic attributes,
can yield the additional information needed to characterize known geothermal resources
and explore for currently undiscovered resources. This study utilizes 2D reflection
seismic data to explore the geophysical signature of the San Emidio geothermal field,
located in NW Nevada. A strong correlation is found between the highly faulted
geothermal field in the San Emidio basin and the geophysical response observed in P-
and S- wave derived attribute sections. A subvertical region of high Poison’s ratio,
associated with less consolidated softer material, as well as locally low P- wave
velocities, associated with higher porosity zones, is found in the vicinity of the
geothermal well where known high permeability faults have been detected. The energy
amplitude versus offset response along the 2D seismic line shows low AVO-gradient
values where faults are interpreted in the seismic data and surficial fault scarps are
extended into the subsurface. The development and application of these techniques to
geothermal resources in the Basin and Range provide opportunities to identify new
subsurface clean energy resources.
Acknowledgements
Include in final draft…
John Louie, Satish, Optim, Graham, Kyle Reeder, Kyle Gray, Andrew Sadowski,
Ken Hoffman and Jasbinsek
Fellow grad students, department members
Family and non-academic friends
John Louie was instrumental in assisting me with the functionality and use of his
codes, scripts and Viewmat processing software. John also provided invaluable
inspiration, guidance and support during the course of this project. Satish
Pullammanappallil provided much assistance producing the velocity models, obtaining
data, and understanding key project details. Funding of this research by Optim Inc. was
crucial in completing this project. Graham Kent provided valued and necessary input on
seismic processing and interpretation. My fellow office mate and masters student Kyle
Reeder, also working on another aspect of this project, provided critical feedback and
stimulating discussions on a daily basis.
Table of Contents
1.0: Introduction (Thesis)
2.0: Paper- 2.1: Introduction - 2.2: Geologic and Tectonic Setting - 2.3: Methods - 2.4: Results and Interpretation - 2.5: Discussion - 2.8: Acknowledgements for Research Article
3.0: Conclusions (Thesis)- 3.1: Future work
- 3.1.1: Look at vibroseis correlations- 3.1.2: Collect better data for pre-critical AVO work- 3.1.3: Collect and integrate well data- 3.1.4: Are there any other future student projects left?- 3.1.5: What could these future projects consist of?
4.0: References
5.0: Appendix - 5.1: San Emidio seismic acquisition details - 5.2: Updated down-going p-wave Zoeppritz Matrix - 5.3: AVO Modeling Matlab script - 5.4: Empirical AVO Matlab script - 5.5: Functions listed
List of Figures
Figure one: Satellite image of the San Emidio geothermal field including the surficial
alterations, range names, well locations, power plant location, seismic line location and
wind mountain epithermal deposit mine location.
Figure two: Image of the Vp and Vs models.
Figure three: Image of the Vp/Vs and Poisson’s ratio sections.
Figure four: Image of the attribute sections, AVO-I, AVO-G and RpRs.
Figure five: Kirchhoff pre-stack depth migrated image of the seismic line with the
production well location, interpreted pyramid sequence horizon, and interpreted faults
overlain.
Figure six: AVO RMS-Energy sections.
Figure seven: Theoretical AVO response out to the critical angle using the full
Zoepppritz equations, Frasier and Richards approximation, and the Shuey approximation.
The horizontal axis is labeled with both angle and offset distance.
Figure eight: Theoretical I/G crossplot including our best estimate and the I/G variability
due to faulting.
Figure nine: A crossplot indicating the change in parameters influence over I/G pair
crossplot location.
Table one: The amplitude preserving processing scheme used in this study.
Introduction (Thesis)
This project began as an effort to expand geothermal resource exploration efforts
to increase energy production at the San Emidio geothermal field. In 2011 the current
field lease owners and power plant operators, US Geothermal Inc., were awarded funds to
explore the ability of advanced geologic and geophysical methods to detect geothermal
resources in the San Emidio basin. Funding for this project was made possible by an
award from the American Recovery and Reinvestment Act (ARRA) for the U.S.
Department of Energy Validation of Innovative Exploration Technologies in the
Geothermal Technologies Program.
The geophysical exploration and software company Optim Inc. was awarded a
contract to lead the advanced seismic methods portion of the project. 10 approximately
3.2 km 2d reflection seismic lines were collected along the southwestern edge of the San
Emidio basin where the known geothermal resource exists. 5 lines were collected near the
existing geothermal field and 5 lines were collected to the north where a right step in the
range front and an epithermal mineral deposit hint at the existence of undeveloped
geothermal resources.
Optim, Inc conducted the initial seismic processing needed to produce high
quality p- and s- wave velocity models used for interpretation and to produce the pre-
stack depth migrated images of the subsurface.
In 2011 surficial geologic mapping along with the pre-stack depth migrated
images were used by University of Nevada Reno masters student, Gregory Rhodes, to
make structural interpretations of the San Emidio geothermal system.
This project continues the geothermal exploration and reservoir characterization
efforts in the San Emidio geothermal system. High resolution compressional and shear
wave velocity models were computed by Satish Pullammanappallil of Optim Inc., and
given to me as ASCII text files.
I then modified a C Shell script written by John Louie, of the Nevada
Seismological Laboratory and the University of Nevada Reno, to convert the ASCII files
into smoothed Intel binary files required for migration. Next, I produced travel time plots
by modifying a travel time generating script written by John Louie. Pre-stack depth
migrations were produced using a C code, written by John Louie. I specified the
migration parameters used by the migration code by modifying a parameter file accessed
by the C code.
Processing of the original shot gathers used in the migrations, and further
processing of the migrated common image gathers, was done by me using John Louie’s
Viewmat software.
I also wrote a library of Matlab scripts and functions to: (1) take information
about rock properties at San Emidio to produce a model of the theoretical seismic
response at the geothermal reservoir, (2) take the p- and s- wave velocity models and
produce various seismic attribute sections, and (3) cross plot empirical data.
John Louie was instrumental in assisting me with the functionality and use of his
codes, scripts and Viewmat processing software. Satish Pullammanappallil provided
much assistance producing the velocity models, obtaining data, and understanding key
project details. Graham Kent provided valued and necessary input on seismic processing
and interpretation.
This paper will be published in _____ under the title, “____”.
BEGIN PAPER
The following chapter is my submission to ___ publication. I am first author on this
paper. My co-authors are: John Louie, and Graham Kent of the Nevada Seismological
Laboratory, University of Nevada, Reno, Nevada and Satish Pullammanappallil of Optim
Inc., Reno, Nevada.
Introduction
The San Emidio Basin is located in the northwestern Basin and Range province,
immediately northeast of the better known Pyramid Lake Basin and approximately 105
km northeast of Reno, Nevada. The San Emidio geothermal system is currently
producing approximately 9.0 MW from 4 (maybe 5 now? Who can I ask?) production
wells located in a highly faulted zone in the southeastern edge of the San Emidio Basin,
currently under lease by US Geothermal Inc. (www.usgeothermal.com). US Geothermal
plans to expand production at San Emidio by drilling additional production wells (Is this
still the case?). Resources to explore and develop the geothermal system in this area
remain limited. Consequently, an understanding of the geothermal system, reservoir
geology and existing data is critical to optimize the development of this field and
minimize future costs.
The current San Emidio power plant is the second power plant to occupy the San
Emidio basin. The first one was a 3.6 MW power plant known as the Empire Geothermal
Plant (http://www.arizonaenergy.org/). In 2012 the current power plant went online with
a design capacity of 11.8 MW (www.usgeothermal.com). Future development at San
Emidio is intended to reach the full capacity of the existing power plant (Reference?
USG?) (Is this paragraph even necessary?).
The current production zone is located within a highly faulted zone in the
southern portion of the San Emidio geothermal resource area (Teplow et al., 2011). To
the north of the proven geothermal resource is a hard-linked right step in the range front
(Rhodes, 2011). In the vicinity of this right step there are indications of a potential high-
grade geothermal resource including a high density of faulting and epithermal mineral
deposits (Rhodes, 2011). Careful characterization of the known reservoir in the southern
part of this system may yield clues to identifying high quality drill targets to the north.
Faults and fractures oriented approximately orthogonal to the least
principle stress direction are favorably oriented for subvertical fluid flow in highly
permeable fault zones (Barton et al., 1995; Faulds et al., 2006). It is believed that
favorably oriented faults and fractures in areas of high fault and fracture density likely
produce the permeability required for deep fluid circulation within the San Emidio
geothermal system (Rhodes, 2011).
Normal faults, favorably oriented for subvertical fluid flow, that have a fault
width of greater than 15 cm are considered to be large aperture faults (LAFs) in the San
Emidio geothermal system (Teplow et al., 2011, Ferrill, D.A., Morris, A.P., 2003).
Production wells at San Emidio have encountered LAFs in the proven geothermal
reservoir (Teplow et al., 2011). For this reason, accurately identifying LAFs at depth is a
crucial step to identifying new geothermal resources in this basin and many other basins
in this region.
Until recently, amplitude versus offset (AVO) methods have been used primarily
to explore and characterize gas accumulations in clastic reservoirs (Rutherford, S.R.,
Williams, R.H., 1989). The development of a comprehensive AVO classification scheme
provides the framework to characterize AVO response in geothermal reservoirs,
independent of hydrocarbon specific responses (Young, R.A., LoPiccolo, R.D., 2003).
Pioneering studies, using AVO methods to characterize geothermal resources in
faulted hard rock reservoirs, have demonstrated the utility of AVO in seismic studies
(Cameli et al, 2000). An increase in fracture density is observed to be associated with a
decrease in formation density and seismic p-wave velocity (Cameli et al, 2000).
Traditional AVO methods, designed for clastic hydrocarbon systems, are often
inappropriate for hard rock geothermal systems and subsequently new AVO methods
must be developed. A new AVO-attribute has been tested and shown to be effective in
detecting fractured geothermal reservoirs in intrusive basement rock, common in many
geothermal systems (Aleardi, M., and Mazzotti, A., 2014 (Personal communication until
published)).
The availability of long offset (~3.3 km) 2D reflection seismic data, at the
producing San Emidio reservoir, provides the unique opportunity to observe and model
the seismic response of such systems in the highly faulted, extensional basins of the
northwestern Basin and Range province. Well constraints on the depth and location of the
known reservoir will be used to observe and characterize the AVO response with the
intent that these results will help provide crucial information about geothermal prospects
to the north.
Geologic and Tectonic Setting
The San Emidio basin is formed between the Lake Range, to the east, and the
Fox Range, to the west. The eastern San Emidio basin, where the known geothermal
resource exists, consists of a Mesozoic basement, Tertiary volcanic and sedimentary
rocks, and Quaternary alluvium, lacustrine sediments and hydrothermally altered rocks
(Rhodes, 2011). The Mesozoic sequence, collectively referred to as the Nightingale
formation and exposed along the western side of the Lake Range, consists of
metamorphosed and folded low-grade argillaceous phyllite, with some slate, schist and
interbedded carbonate, sandy and volcanic horizons (Moore, 1979; Wood, 1990). The
overlain Tertiary volcanic and sedimentary sequence consists of the middle Miocene
Pyramid sequence volcanic rocks and late Miocene sedimentary rocks correlated to the
Truckee Formation (Drakos, 2007; Moore, 1979). Quaternary sediments, including
alluvial fan deposits and Pleistocene Lake Lahontan silt, sands, tufa, and silicified sands
are observed at the surface along the western edge of the Lake Range (Teplow et al.,
2011).
2.2.1: Structural framework
The San Emidio fault zone consists of a network of north and north-northeast
striking normal faults that dip to the west and west-northwest respectively. The western
flank of the Lake Range is bound by the north striking, west dipping Lake Range normal
fault. The Lake Range consists of a series of well exposed, primarily east-tilted, north-
trending fault blocks bounded on the west by the moderately to steeply west-dipping
Lake Range fault. To the west, on the hanging wall of the Lake Range fault, Quaternary
sediments overlie the Tertiary and Mesozoic basement complex.
An approximately 1 km right-step in the northern section of the Lake Range fault
is connected by an east-northeast striking sinistral-normal oblique-slip fault. The regional
extension direction, inferred from geodetic data, and the slip direction of the oblique-slip
fault linking the right-step in the Lake Range, taken from preserved slickensides along
the fault scarp, both trend to the west-northwest (Teplow et al., 2011). An epithermal
gold and silver deposit resides in the intensely silicified intersection of multiple normal
faults extending northward of the right-step.
The roughly north-trending, west-dipping, curvilinear Holocene San Emidio fault
is located approximately 1 km west of the Lake Range fault in the southern San Emidio
basin. The current San Emidio geothermal power plant is producing from a reservoir
located at the top of the middle Miocene Pyramid sequence, located at the southernmost
surficial expression of the San Emidio fault (Rhodes, 2011).
Production wells in the San Emidio geothermal field are located in the basin,
adjacent to fault and spring-related hydrothermal surface alterations and near surface
carbonate and silica deposits. Additionally, the production wells are located near the
intersection of multiple normal faults.
- 2.2.2: Kinematic analysis
Slip and dilation tendency analysis, in the San Emidio desert, suggests that north-
northeast striking faults and fractures are favorably oriented for fluid flow due to west-
northwest-directed extension (Rhodes, 2011). Both the Lake Range fault and San Emidio
fault trend roughly northward and are favorably oriented for fluid flow at depth.
Is there anything else you’d like to see here?
Methods
This study utilizes 2-d seismic reflection data from an ~3.3 km east-west trending
line. Data was recorded using 3-component geophones with a ~17 m geophone spacing
and a vibroseis source with a ~67 m shot spacing. The seismic line used in this study was
chosen due to its close proximity to the known geothermal resource and the four
productions wells in the San Emidio field. This east-west trending seismic line extends
from the Lake Range fault to the east, through the north-trending San Emidio fault, into
the center of the basin to the west.
- 2.3.1: Velocity Model: Vp, Vs and Derivative Sections
The fidelity of the results at every step in this study is contingent upon the
accuracy of the p- and s- wave velocity modeling process. For this reason, a significant
emphasis has been placed on determining the most accurate velocity models possible.
The p-wave and s-wave velocity models were computed by Optim Inc. using a simulated-
annealing algorithm that utilizes a Monte Carlo optimization scheme to invert first-arrival
picks for velocities (Pullammanappallil, S.K., Louie, J.N., 1994). The complex structure
at San Emidio requires a non-linear velocity optimization that avoids assumptions about
structural geometry. The simulated-annealing algorithm iteratively converges on an
optimized solution, while avoiding becoming fixed at local least-square error minima
points. This method allows for an accurate velocity model in the structurally complex San
Emidio basin. Travel time plots are constructed using a fast finite-differencing scheme
that utilizes a solution to the eikonal equation (Vidale, 1988). Velocity data from the ~3.3
km seismic reflection line was split into a grid that is 400 elements wide by 200 elements
deep. Each square element has a length of 8.382 m.
The p- and s- wave velocity models were used to compute the Vp/Vs and
Poisson’s Ratio values at each element location, corresponding to each subsurface
location in the velocity model. A max Vp/Vs value of 4 was used to clip any anomalously
high Vp/Vs values. A minimum Poisson’s Ratio value of 0.13 was used to clip any
anomalous near zero values. The Vp/Vs ratio has a theoretical limit of infinity, making its
range quite large, while Poisson’s ratio has an upper limit of 0.5 for isotropic rocks
(Gercek, H., 2007). Despite the physical significance of Poisson’s ratio, it is essentially a
non-linear scaling of Vp/Vs that can bring out features that are not easily observed in
some large range Vp/Vs sections (Chopra, S., Castagna, J.P., 2014).
AVO Intercept and Gradient attribute sections were computed using the Smith
and Gidlow approximation to the Zoeppritz equations (Smith, G.C. and Gidlow, P.M.,
1987). The Smith and Gidlow approximation requires the p- and s- wave velocity above
and below an impedance contrast to compute the reflection coefficients with offset. An
Rp+Rs attribute section was also implemented as another method to potentially enhance
seismic exploration of fractures in faulted basement rock (should I expand on this?)
(Aleardi, M., and Mazzotti, A., 2014 (Personal communication until published)). The
AVO- Intercept, Gradient and Rp+Rs attribute sections use the p- and s- wave velocity
values from the simulated annealing velocity models. A maximum and minimum clip
value was empirically chosen to smooth anomalous values and improve section image
quality.
- 2.3.2: Seismic processing: Imaging / Amplitude
We designed our seismic processing scheme to optimize our ability to image
tectonic structure and preserve amplitudes in our pre-stack depth migrated (PSDM)
common image gathers (CIGs) for use in our AVO analysis. The objective was to
suppress noise and isolate the reflectivity events of interest. The Viewmat seismic
processing software developed by John Louie at the University of Nevada Reno was used
to process the seismic gathers in this study.
The processing scheme, shown in table one, applies a spherical divergence
correction to adjust for amplitude attenuation due to geometric spreading of the wave
front. The imaging scheme applies a trace equalization step to balance the amplitudes
from trace to trace. A dip filter and time-invariant band pass filter were applied, in both
cases, to remove high amplitude shear wave contamination and increase the signal to
noise ratio of our p-wave reflections.
This study implemented a Kirchhoff pre-stack depth migration (KPSDM) to
preserve amplitude and properly migrate seismic energy in CIG-offset space. The
KPSDM algorithm used in this study has been shown to image steeply dipping, near
vertical, structures in both synthetic and real data (Louie et al, 1988). KPSDM is
generally considered to produce reliable reflection amplitudes in areas of moderate
structural complexity, making it an ideal candidate for AVO analysis (Feng, H., Bancroft,
J.C., 2006). Migrations were achieved across a 400-by-200 element x-z plane, identical to
the velocity model, with 51 elements in the offset dimension at each x-location. A
migration anti-aliasing operator was applied to reduce the aliasing effect produced when
the operator dip along the migration summation path is too steep for a given input seismic
trace spacing and its frequency content (Lumley et al., 1994).
- 2.3.3: AVO CIG-Offset RMS-Energy Amplitude Extractions
Pre-stack depth migrated common image gathers were not of sufficient quality to
accurately measure the AVO signature of any one coherent reservoir reflector.
Consequently, a root mean square (RMS)-energy approach was taken to observe the
magnitude of seismic energy, reflected at different offsets, at a range of depths across the
entire 2d seismic line.
An RMS smoothing kernel was used to produce RMS-common image gathers
from the original seismic amplitude common image gathers. Various depth ranges,
ranging from the bottom to the top of the reservoir interval, were used to create amplitude
extractions across the seismic line. Vertical stacking schemes, including summing and
highest/lowest value extractions, were implemented to collapse the 3d CIG location-
offset-depth cube into a CIG location-offset section.
- 2.3.4: AVO modeling: Pre- and Post- critical, Crossplotting
AVO modeling was designed for two purposes: 1) to see if changes in rock
properties, within the reservoir, would feasibly produce an AVO response that is
distinguishable in real data, and 2) to use existing geologic and geophysical data to
estimate the AVO response within the reservoir. The data available limit us to construct
1D, 2-layer, elastic and isotropic models of reflectivity versus offset. Consequently,
anisotropic effects on offset related reflectivity are not taken into consideration in these
models.
Rock property input values were taken from a variety of geologic and geophysical
sources. Consequently, a best estimate of each parameter was chosen and the variability
was used as input to our sensitivity analysis. In the absence of sonic log well data, we
used information from our velocity models to estimate a range of compressional and
shear wave velocities reasonable within our reservoir zone. Density value estimates were
taken from values determined by a basin scale gravity study and density values measured
in laboratory conditions as reported in two UNR student thesis (Drakos, 2007;
Mankhemthong N., 2008; Teplow et al., 2011).
Reflection coefficients were computed using both the Zoeppritz equations and the
Shuey approximation to the Zoeppritz equations (Shuey, 1985). Compressional wave
incident angle ranges, for the Shuey approximation, were chosen to be less than 10
degrees from our critical angle to stay within the range of fidelity for the approximation
(Chopra, S., Castagna, J.P., 2014). Offset-dependent reflectivity determined using the
Zoeppritz equations uses incident angles ranging from zero to ninety degrees. The Shuey
AVO response, between zero and twenty degrees, shows near identical agreement with
the Zoeppritz equations in all models used in this study (Only mention in results?).
Accurately determining the AVO intercept and gradient terms is essential to
determine a background trend and identify AVO anomalies, either in those attributes
alone or crossplotted against each other. The intercept and gradient attributes can be
derived directly from the terms of the Shuey approximation or they can be calculated by
taking the least squares regression of the reflection coefficients versus the sine-squared of
the offset (Chopra, S., Castagna, J.P., 2014; Young, R., LoPiccolo, R., 2005). Once the
intercept and gradient terms are calculated they can be crossplotted against each other
and the AVO response can be characterized by the methods of Young and LoPiccolo
(2003).
An AVO sensitivity analysis was conducted in order to measure the variation in
AVO response due to parameter uncertainty. A range of density, compressional wave,
and shear wave velocity values were used to compute the AVO response for each
combination of values and the results were examined in intercept-gradient crossplot and
reflection coefficient versus offset space.
Results and Interpretation
- 2.4.1: Velocity Models and Derivatives
The p-wave velocity model, seen in figure two, shows the velocity model
gradient extending and deepening towards the basin fill to the west. A prominent low p-
wave velocity zone, and velocity inversion zone, is observed towards the center of the
seismic line, immediately east of the geothermal production well. Below the low velocity
zone there is also a substantial deepening of the velocity gradient. A series of steeply
west dipping steps in the velocity gradient are observed dipping toward the basin. A thin
region of slightly higher velocities is observed extending upward just west of the low
velocity zone.
The s-wave velocity model, seen in figure two, has similar characteristics to the p-
wave velocity model but key differences are notices. The s-wave model shows
substantially higher velocities and a steeper velocity gradient in the eastern third of the
velocity model. The depth of the higher velocities and steeper velocity gradient is
primarily flat and drops of in a near vertical manner as it extends westward towards the
basin. This velocity model shows two low velocity zones separated by a slightly higher
velocity zone. The location of the eastern low velocity zone and the adjacent higher
velocity zone to the west are found in the same general area in both the p- and s- wave
velocity models.
Figure 3 shows the Vp/Vs and Poisson’s ratio sections derived from the velocity
modeling process. Since there is a direct relationship between Vp/Vs and Poisson’s ratio
values, and differences between the two sections are due to a non-linear scaling, they will
be discussed together. In general, we notice that the Vp/Vs section is more sensitive to
higher values, while the Poisson’s ratio section is more sensitive to the lower values,
rendering both sections useful in the interpretation process.
In hard rock Poisson’s ratio is known to vary with effective pressure, fluid
saturation, the aspect ratio of fracture pores, and the density of fracture pores in a rock
mass (Zhang, J.J., Bentley, L.R., 2005). Inverting seismic velocities measured
experimentally, results for effective moduli and pore aspect ratio can be determined using
the theoretical model of Kuster and Toksoz (1974), derived from scattering theory
(Zhang, J.J., 2001; Zhang, J.J., Bentley, L.R., 2005). At an effective pressure of 10 MPa,
round pores, both dry and wet, do not significantly influence Poisson’s ratio (Zhang, J.J.,
Bentley, L.R., 2005). In fractured rock, at the same effective pressure, a decrease in
aspect ratio will decrease Poisson’s ratio in dry rock and increase Poisson’s ratio in wet
rock (Zhang, J.J., Bentley, L.R., 2005). These finding are applicable to the San Emidio
geothermal system because the 10 MPa used in the Zhang et al (2001) study is very
similar to the 9.81 Mpa estimated for the San Emidio reservoir (see appendix 4.1). It has
also been observed that in most instances poorly consolidated and/or brine-saturated
rocks tend to have a high Poisson’s ratio, while highly lithified rocks tend to have low
values of Poisson’s ratio (Chopra, S., Castagna, J.P., 2014).
Using Poisson’s ratio attribute data derived from compressional and shear wave
velocity modeling along with inversion data of rock physics studies we can begin to make
informed interpretations of the San Emidio geothermal system. Near the range front, to
the east of the Poisson’s ratio section, at depths greater than ~250 m values are estimated
to be low, below 0.27, which is consistent with what would be expected from the
metasedimentary nightingale bedrock formation. Poisson’s ratio values, to the west in the
deeper part of the basin are higher, ranging from 0.35 to 0.45, which is consistent with
poorly consolidated alluvial and lacustrine lithologies (Gercek, H., 2007).
In the central portion of the section, at the base and east of the production well,
we observe locally high values of Poisson’s ratio, varying between 0.38 and 0.41. High
values of Poisson’s ratio are consistent with the low aspect ratio wet fractured rock
confirmed by the presence of large aperture faulting and geothermal fluids during well
drilling operations and production (Teplow et al., 2011; Rhodes, 2011). Lower values of
Poisson’s ratio, ranging between 0.27 and 0.33 are encountered in the middle and upper
portion of the well path, which is consistent with the precipitation of minerals from the
cooling of geothermal fluids as observed in the chalcedony cap encountered in the near
surface during drilling of the well (Teplow et al., 2011; Rhodes, 2011) (is a more specific
depth required here?).
To the west of the production well we observe a region of low Poisson’s ratio,
ranging from 0.22 to 0.33, which may indicate the presence of rock fractures with
considerably less pore saturation and/or a generally harder lithology than the basin rocks
to the west. Below roughly 70% of the surficial faults mapped in earlier studies, we
observe additional low values of Poisson’s ratio and Vp/Vs ratios at depths less than 500
m, which may suggest harder rock due to hydrothermal alterations and/or fractured dry
rocks (Rhodes, 2011).
AVO Intercept and Gradient sections
Rp+Rs sections
- 2.4.2: Imaging and Structure
Accurately identifying large aperture normal faults at depth in the San Emidio
geothermal system is key to detecting economic geothermal resources. These faults can
be observed as seismic discontinuities, in both stacked and unstacked data. The acoustic
impedance contrast between the alluvial and lacustrine basin fill and the pyramid
sequence volcanic rocks produces a strong reflector, supported by drilling data, that can
be examined for evidence of faulting.
Figure 5 shows a Kirchhoff pre-stack depth migrated image of the seismic line
with the production well location, interpreted pyramid sequence horizon, and interpreted
faults overlain. 5 faults are interpreted in the center of the seismic section. Faults are
interpreted by identifying discontinuities in otherwise coherent reflectors and observing
vertical offset in reflectors. Interpreted faults have dips ranging from 65 to 69 degrees.
Interpreted faults in the seismic section are consistent with surficial fault mapping
(Rhodes, 2011).
What structure is depicted in our images?
What new info is shown?
What are the limiations?
- 2.4.3: RMS-Energy AVO Plots
The…
We see the high AVO amplitudes in the reservoir zone due to the strong
interpreted basin fill – pyramid sequence volcanics impedance contrast.
Faults can be identified in the AVO RMS-Energy plots.
Explain how we identify the faults
- 2.4.4: AVO Modeling
Our AVO modeling targets the seismic impedance contrast between the basin fill
sedimentary rocks and the pyramid sequence volcanic rocks. Drill data confirms the
depth of this boundary to be 500 m in the vicinity of the well. Using velocity and density
estimates the refraction crossover distance is modeled to be roughly 2.2 km, which is in
good agreement with the 2.0 to 2.2 km observed in the RMS-energy AVO plots.
Figure 7 shows the p-wave reflection amplitude response using pre-critical
offsets, ranging from 0 to 30 degrees, for an incident p-wave modeled with the Shuey
approximation, the Richards and Frasier approximation, and the full Zoeppritz equations.
The AVO response shows a positive amplitude reflection at zero-offset that decreases in
amplitude with increasing offset.
The AVO response predicted with the Shuey approximation shows near identical
agreement to the full Zoeppritz equations from 0 to 20 degrees, consequently this offset
range was used to determine the AVO- intercept and gradient terms. While the reflection
at zero offset is predicted to be positive the AVO-gradient term is negative and predicts
near zero to negative amplitude reflections at far offsets.
Figure 8a shows our best estimate of the AVO intercept-gradient response
between the sedimentary basin fill and the pyramid sequence volcanic rocks. The Young
and LoPiccolo AVO intercept-gradient classification scheme characterize this as a type 1
response. This response is expected to be characteristic of the overall background trend in
seismic data with a high signal to noise ratio. Once a background trend has been
established, deviations from this background trend can provide information about
changes in rock properties at depth.
Deviations from the background trend, caused by lower density and velocity
values associated with rock fractures, are estimated and shown in Figure 8b. Within the
top basin fill sedimentary layer compressional wave velocity varied between 2,111 m/s
and 2,202 m/s, shear wave velocity varied between 1,113 m/s and 1,204 m/s, and density
varied between 1.8 g/cc and 2.0 g/cc. Within the lower pyramid sequence volcanic layer
compressional wave velocity varied between 2,332 m/s and 2,423 m/s, shear wave
velocity varied between 1,250 m/s and 1,341 m/s, and density varied between 2.45 g/cc
and 2.65 g/cc. These variations in rock properties, within the reservoir, produced an AVO
response that would be distinguishable in real seismic data of sufficient quality.
Figure 8c provides a summary of how a change in density, shear wave velocity
and compression wave velocity influences the location of the AVO intercept-gradient
pair. A decrease in shear wave velocity results in an increase in the gradient value and no
change in the zero-offset reflection amplitude. A decrease in the density value results in a
decrease in the zero-offset reflection amplitude and an increase in the gradient value. A
decrease in compressional wave velocity results in a decrease in the gradient value and a
decrease in the zero-offset reflection amplitude.
Discussion
The…
Discuss the significance of the results and interpretations
AVO crossplotting can provide information about porosity and Poisson’s ratio can
provide information about the aspect ratio of the pores (faulting), pore saturation,
and may help support evidence of certain rock lithologies.
What considerations need to be made during interpretation at all levels.
Traditional pre-critical AVO analysis doesn’t work in our data set, why not?
Future: Collect better data for pre-critical AVO work
Future: Collect and integrate well data
Acknowledgements
I would like to thank Optim Inc. for their generous support of our research
and for providing the P- and S- wave velocity models. I would also like to thank US
Geothermal for allowing use to use and publish data collected for their San Emidio
geothermal project. I would also like to thank the Nevada Seismological Laboratory for
funding and support of this research.
END of PAPER
Conclusion
The…
- Take away points and where to go from here
Appendix
4.1: San Emidio seismic acquisition details
Basic Information
Number of lines = 10
Type of data = 2D seismic reflection
Geophone group spacing = 55 ft
Shot (vibroseis) spacing = 220 ft
CMP spacing = 55/2 ft = 27.5 ft
~49 shot points per line
Record length = 6 seconds
Sampling rate = 2 ms
Nyquist frequency = 250 Hz
Channels = 193
Hydrostatic pressure due to overburden is assumed to be 9.81 MPa assuming
(Pressure) = (depth)*(density)*(gravity) = (500m)*(2000kg/m3)*(9.81m/s2)
Line 7
This line runs roughly E-W in the south of the basin.
Line 7 is the closest line to the current geothermal production wells.
This line is located directly above three existing production wells trending
roughly N-S.
4.2: Updated down-going p-wave Zoeppritz Matrix
- Insert corrected version of the down going p-wave Zoeppritz Matrix
4.3: AVO Modeling Matlab script
- Insert modified version of the script with detailed descriptions
4.4: Empirical AVO Matlab script
- Insert modified version of the script with detailed descriptions
4.5: Functions listed
- Insert modified version of the script with detailed descriptions
References
Aleardi, M., Mazzotti, A., 2014, A feasibility study on the expected seismic AVA
signatures of deep fractured geothermal reservoirs in an intrusive basement,
Journal of Geophysics and Engineering, Preprint
Cameli, G.M., Ceccarelli, A., Dini, I., Mazzotti, A., 2000, Contribution of the seismic
reflection method to the location of deep fractured levels in the geothermal fields
of southern Tuscany (Central Italy), Proceedings World Geothermal Congress
2000, p. 1025-1029
Casini, M., Ciuffi, S., Fiordelisi, A., Mazzotti, A., Stucchi, E., 2010, Results of a 3D
seismic survey at the Travale (Italy) test site, Geothermics, v.39, p.4-12
Castagna, J.P. and Backus, M.M., 1993, AVO analysis-tutorial and review, in Castagna,
J. and Backus, M.M., eds, Offset-dependent reflectivity - Theory and practice of
AVO analysis: Soc. Expl. Geophys., 3-37
Castagna, J.P., Swan, H.W., 1997, Principles of AVO crossplotting, The Leading Edge,
v.16, no. 04, p.337-342
Chopra, S., Castagna, J.P., 2014, AVO, Investigations in geophysics No. 16, Society of
exploration geophysicists
Drakos, P.S., 2007, Tertiary Stratigraphy and Structure of the southern Lake Range
northwest Nevada: Assessment of kinematic links between strike-slip and normal
faults in the northern Walker Lane, University of Nevada Reno Masters Thesis
Faulds, J.E., Coolbaugh, M.F., Vice, G.S., Edwards, M.L., 2006, Characterizing
structural controls of geothermal fields in the northwesten Great Basin: A
progress report, GRC Transactions, v.30, p.69-76
Feng, H., Bancroft, J.C., 2006, AVO principles, processing and inversion, CREWES
Research Report, v.18, p.1-19
Gercek, H., 2007, Review: Poisson’s ratio values for rocks, International Journal of Rock
Mechanics & Mining Sciences, v. 44, p. 1-13.
Han, D.H., 1986, Effects of porosity and clay content on acoustic properties of
sandstones and unconsolidated sediments: Ph.D. dissertation, Stanford University
Kreemer, C., Blewitt, G., and Hammond, W.C., 2006, Using geodesy to explore
correlations between crustal deformation characteristics and geothermal
resources: Geothermal Resources Council Transactions, v. 30, p. 441-446.
Kreemer, C., Blewitt, G., and, Hammond, W.C., 2009, Geodetic constraints on
contemporary deformation in the northern Walker Lane: 2. Velocity and strain
rate tensor analysis, in Oldow, J.S, and Cashman, P.H., eds., Late Cenozoic
structure and evolution of the Great Basin-Sierra Nevada transition: Geological
Society of America Special Volume 447, p. 17-31.
Louie, J.N., Clayton, R.W., LeBras, R.J., 1988, Three-dimensional imaging of steeply
dipping structure near San Andreas fault, Parkfield, California, Geophysics, v. 53
no.2, p. 176-185
Lumley, D.E., Claerbout, J.F., Devc, D., 1994, Anti-aliased Kirchhoff 3-D migration,
SEG Expanded Abstract
Luschen, E., Wolfgramm, M., Fritzer, T., Dussel, M., Thomas, R., Schulz, R., 2014, 3D
seismic survey explores geothermal targets for reservoir characterization at
Unterhaching, Munich, Germany, Geothermics, v.50, p.167-179
Mankhemthong, N., 2008, Structure of the inter-basin transition zone between Dixie
Valley and Fairview Valley, Nevada, USA, University of Nevada Reno Masters
Thesis
Moore, J.N., 1979, Geology map of the San Emidio geothermal area: U.S. DOE:
Department of Geothermal Energy, 78-1701.b.1.2.2, 8 p.
Ostrander, W.J., 1984, Plane-wave reflection coefficients for gas sands at nonnormal
angles of incidence, Geophysics, v.40, no.10, p,1637-1648
Pullammanappallil, S.K., Louie, J.N., 1994, A generalized simulated-annealing
optimization for inversion of first-arrival times, Bulletin of the Seismological
Society of America, 84, p. 1397-1409
Rhodes, G.T., 2011, Structural controls of the San Emidio geothermal system,
northwestern Nevada, University of Nevada Reno Masters Thesis
Rutherford, S.R., Williams, R.H., 1989, Amplitude-versus-offset variations in gas sands,
Geophysics, v.54, no. 06, p.680-688
Shuey, R.T., 1985, A simplification of the Zoeppritz equations, Geophysics, v.50, p.609-
614
Smith, G.C. and Gidlow, P.M., 1987, Weighted stacking for rock property estimation and
detection of gas: Geophys. Prosp., v. 35, p. 993-1014
Teplow, W., Faulds, J., Rhodes, G., Moeck, I., Eneva, M., Pullamannapallil, S., 2011,
Finding large aperture fractures in geothermal resource areas using three-
component long-offset surface seismic survey, PSInSAR and kinematic structural
analysis, US Geothermal Report.
Vidale, J., 1988, Finite-difference calculation of travel times, Bulletin of the
Seismological Society of America, v.78, no.6, p.2062-2076
Wood, J.D., 1990, Geology of the Wind Mountain gold deposit Washoe County, Nevada,
in Raines, G.L., Lisle, R.E., Schafer, R.W., and Wilkinson, W.H., eds., Geology
and ore deposits of the Great Basin: Symposium proceedings: Geological Society
of Nevada, p. 1051-1061.
Young, R.A., LoPiccolo, R.D., 2003 A comprehensive AVO classification, The Leading
Edge. October 2003
Zhang, J.J., 2001, Time-lapse seismic surveys: Rock physics basis, University of Calgary
Masters Thesis
Zhang, J.J., Bentley, L.R., 2005, Factors determining Poisson’s ratio, CREWES Research
Report, v. 17, p.1-15
“San Emidio” US Geothermal Inc., accessed March 2nd, 2015.
http://www.usgeothermal.com/projects/3/San%20Emidio
“U.S. Geothermal Completes Acquisition of Producing Geothermal Power Plant and
Energy Rights in Nevada”, accessed March 2nd, 2015.
http://www.arizonaenergy.org/News_08/News_May08/U.S.%20Geothermal%20
Completes%20Acquisition%20of%20Producing%20Geothermal%20Power%20P
lant%20and%20Energy%20Rights%20in%20Nevada.htm