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

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