USING ELECTRICAL SURVEY TECHNIQUES TO IDENTIFY DRILLING TARGETS IN BASIN AND RANGE GEOTHERMAL PROSPE

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USING ELECTRICAL SURVEY TECHNIQUES TO IDENTIFY
DRILLING TARGETS IN BASIN AND RANGE GEOTHERMAL
PROSPECTS

• John Pritchett
• Science Applications International Corporation

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Motivation

• The first deep exploratory well in a new
  greenfield prospect is likely to cost several
  million dollars.
• But experience shows that the chances of the well
  being a commercial success are only one in
  five.
• Improving these dismal odds, even if only
  slightly, would make geothermal prospecting and
  project development a much more attractive
  investment.
• Where should the first deep well be sited?

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Computational Tools

• Since 1992, six geophysical postprocessors have
  been developed for SAICs STAR geothermal
  reservoir simulator.
• The postprocessors compute temporal changes in
  geophysical observables due to subsurface field
  evolution caused by geothermal production, as
  previously computed by the simulator.
• The purpose is to constrain history matching
  studies by considering time-lapse geophysical
  survey data, thereby providing more robust
  predictive models.

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Geophysical Postprocessors

• The postprocessors are
• Microgravity
• Active seismic
• DC resistivity
• MT resistivity
• CSAMT resistivity
• Self-potential (SP)

The ones in red were used for the present study
and are suitable for operation in static mode
(as well as in time-lapse mode).
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Reservoir Models

• Eight different hypothetical Basin and Range-type
  reservoirs were simulated numerically.
• Four are normal, and four are hidden
  reservoirs.
• Normal means that the hottest part of the
  subsurface resource directly underlies the
  visible surface manifestations (hot springs,
  fumaroles, etc.).
• Hidden means that the primary deep thermal
  anomaly is displaced a few kilometers away
  laterally from the surface evidence for
  geothermal activity.

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Simulating the Stable Reservoir

• The only differences in input specifications
  among the eight computed cases are the boundary
  conditions that were prescribed along the base of
  the computing volume, at five kilometers depth.
• All eight cases were carried out to t 100,000
  years to allow a stable natural state to
  develop.
• Area is 100 km2 a 6-km-wide basin is bounded by
  north-south mountain ranges and rangefront faults.

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System Geometry (all eight cases)
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For Further Information

• A comprehensive final report describing this
  study
• Finding Hidden Geothermal Resources in the
  Basin and Range Using Electrical Survey
  Techniques A Computational Feasibility Study,
  by J. W. Pritchett
• has been published and is available for
  download in electronic form from the INL internet
  website
• http//geothermal.inel.gov/publications.sh
  tml

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Typical Results

• Two of the eight cases (one normal and one
  hidden) were selected for detailed discussion
  in this presentation, since they would be hard to
  distinguish from each other based on visible
  surface manifestations and shallow drilling
  evidence alone.
• Deep upflow at 5 km depth
• Normal case 100 t/h of 3000 ppm NaCl brine
  at 255ºC flows upward into the Eastern Fault
  Zone.
• Hidden case 100 t/h of 3000 ppm NaCl brine
  at 345ºC flows upward into the Western Fault Zone.

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Stable Reservoir Temperature East-west
vertical section passing through the Thermal
Area.Temperature contour spacing (red) is
10oC.Yellow high-permeability formations.
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Natural Surface DischargeOutflow rates from the
Thermal Area, whichaccounts for gt 70 of total
study area outflow
Hidden Normal Reservoir
Reservoir Hot Water 87 t/h 93
t/h Steam 12 t/h 14 t/h
Total Flow 99 t/h 107
t/h (Note 1 t/h 0.28 kg/s 2.2 kph)
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Heat Flow Survey ResultsTemperature at 300 m
depth contours are 70oC, 100oC, 130oC and 160oC
(14, 21, 29 and 36 HFU).
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Deep Thermal Area Drilling
If, based on natural surface manifestations and a
300-m heat flow survey, a 2.5 km discovery well
is drilled in the Thermal Area A useful resource
will be found in the normal reservoir. But the
hidden reservoir will be disappointing.
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MT Apparent Resistivity Surveys

• Low apparent resistivity correlates with
• high temperature
• high salinity
• high permeability/porosity
• Higher frequency gt better spatial resolution
• Lower frequency gt greater penetration depth

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MT Survey Results at 0.1 HzApparent resistivity
contour spacing is 5 ohm-meters.
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Self-Potential (SP) Surveys

• Liquid flow through rock gt drag current.
• Drag current gt electrical potential gradient
  (Ohms Law).
• Positive anomaly ltgt upflow area
• Negative anomaly ltgt downflow area

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SP Survey ResultsContour spacing is 20
millivolts. X marks SP maximum.
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Recommendations

• To discriminate hidden from normal
  reservoirs, wide-ranging (several km) and
  deeply-penetrating electrical surveys are needed.
• SP surveys should cover entire region.
• MT surveys should focus on low frequencies.
• Large-scale superimposed anomalies in both
  deep electrical resistivity and self-potential
  are strong indicators, and should be considered
  when siting the first deep discovery well.

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Redundancy

• MT and SP anomalies can occur for various
  reasons
• Clays can exhibit low resistivity, but are
  usually relatively impermeable and will not host
  the strong upflows that result in large-scale SP
  signatures.
• Cold fluid flows can also cause SP anomalies, but
  ordinary groundwater aquifers will not usually
  exhibit abnormally low electrical resistivity.
• If regional heat flow is high and the MT and
  SP anomalies coincide, the overlap area is the
  most promising target for the first deep
  discovery well.

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Effectiveness

• Estimated costs to carry out SP and MT surveys
  on the spatial scales considered in this study
  are less than the probable cost of drilling a
  single deep (2-3 km) unsuccessful exploratory
  well,
• by approximately one order of magnitude.

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Acknowledgments

• Considerable valuable technical help was provided
  for the author during the course of this work by
  (in alphabetical order) Jim Combs, Sabodh Garg
  and Mike Shook, and is gratefully acknowledged.
• This research study was supported in part by the
  United States Department of Energy Geothermal
  Technologies Program under Contract Number
  00018084, Bechtel BWXT Idaho, LLC (BBWI).