Public Service of Colorado Ponnequin Wind Farm Fracture … · 2014-02-11 · 1 | US DOE Geothermal...
Transcript of Public Service of Colorado Ponnequin Wind Farm Fracture … · 2014-02-11 · 1 | US DOE Geothermal...
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Public Service of Colorado Ponnequin Wind Farm
Geothermal Technologies Program 2010 Peer Review
Fracture Characterization in Enhanced Geothermal Systems by Wellbore and Reservoir Analysis
Roland HorneStanford University
Reservoir CharacterizationJune 28, 2010
This presentation does not contain any proprietary confidential, or otherwise restricted information.
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Project Overview
1. In-situ Multifunctional Nanosensors for Fractured Reservoir Characterization.
2. Characterization of Fracture Properties using Production Data.
3. Fracture Characterization by Resistivity Tomography.
EGS is all about the fractures:• Where are they?• What are their properties?• How will they perform during energy extraction?
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Investigate a new tool (nanosensors) to measure:
Pressure and temperature anywhere in the formation.
Fracture aperture.
Develop a method to estimate reservoir parameters and characterize fracture networks based on these measurements.
1. Nanosensors - Objectives
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Initial testing: Investigate the feasibility of transporting
various nanomaterials through porous media (rocks, long
flow path).
Develop (fabricate) functional pressure- and
temperature-sensitive nanoparticles.
Study Phases
NOW...
NEXT STEPS...
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(A) Cylindrical to spherical shape transformation due to temperature change
Initial shape
At specific temperature
(B) Multi-layer nanoparticle with different coating thickness
Initially with all layers At 150 °C At 250 °C At 350 °C At 450 °C
Initially with all nanowires
Nanowires melted due to temperature effect
(C) Spherical nanoparticle with nanowires attached to its outer surface
A. Cylindrical to spherical shape transforming nanoparticles.
B. Multilayer different coating thickness nanoparticles.
C. Inert nanosphere with temperature sensitive nanowires.
Temperature-Sensitive Nanotracers
Now investigating various shapes/materials.
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Preliminary Testing
Can we pass solid particles through the pore spaces within
a reservoir? What shape/material?
Spherical SiO2
Berea sandstone
10 m sand-packed slim tube
Nonspherical silver nanowires
Berea sandstone
Nonspherical Hematite nanorice
Berea sandstone
glass beads
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SiO2 nanoparticles within pore spaces – Berea-I
Fracture
Nanoparticles
Rock fine
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Pore size Distribution
Berea - SiO2 Milestone
0
0.1
0.010.1110100
Dif
f. in
trus
ion
of
Mer
cury
(ml/g
/µm
)
Pore diameter (µm)
Particle size determination
Influent
Effluent
Fracture
Nanoparticles
Within pore spaces
*Scanning Electron Microscopy imaging SEM
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Berea - SiO2 Milestone
http://abclocal.go.com/kgo/video?id=6889459ABC News, June 2009
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Berea - SiO2 Milestone
http://abclocal.go.com/kgo/video?id=6889459ABC News, June 2009
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Specific objective
To study the transport and recovery of
injected SiO2 nanoparticles through a
longer flow path.
Imitates near-field interwell distance as in
the conventional interwell tracer test.
Main result
Nanoparticles were recovered.
10 m slim tube Nanofluid vessel
Slim Tube - SiO2 Milestone
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Investigate recovery through longer flow path (near-field interwell conditions)
Slim Tube - SiO2 Milestone
Visual inspectionEffluent samples
Scale: at 12,000x5 µm
0
5
10
15
20
25
30
35
0.0 0.5 1.0 1.5 2.0
Inte
nsity
(%)
Post injected PVI
Dynamic light scattering
Bulk of Nanoparticle started and ceased production at half and 1.5 PVI, respectively.
Start producing
Ceased production
Bulk of NP
Confirmed by SEM
SEM imaging
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Berea - Silver Nanowires
Investigate recovery of wire-like nanoparticles
00.02
0.040.06
0.080.1
0.120.14
0.160.18
0.2
300 350 400 450 500 550 600 650 700Wavelength - nm
Abs
orba
nce
(%)
Original AgNW- Prior to Inj
AgNW_1.10- During NW Inj
AgNW_8- 1st PVI P.I.
AgNW- 2nd PVI P.I.
AgNW- 3rd PVI P.I.
AgNW- 6 PVI P.I.
AgNW - 13th PVI P.I.
UV-visible Spectroscopy
Influent UV signature
effluents UV’s
Note: The red curve is the right optical
signature of silver nanowires. Effluent samples
show pure water behaviour.
Core
3 mm
5.8 cm
3.8 cm
Front-view Side-view
Front side
Back-side
Front-side
Inlet
A
B
B
SEM imaging of front face
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Glass Beads - Hematite
Hematite (nanorice) Influent
Injection into glass beads
A glass bead at the inlet side
On the surface of glass bead Sandstone at inlet (front face)Is it core related issue (clay, etc…)?
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Surface Interactions
Hypothesis Transport of nanoparticles (especially nonspherical) is limited by
interactions that reduce surface energy by effectively reducing surface area Aggregation of particles, which leads to bridging of pores “Sticking” to pore walls
Nonspherical particles are more prone to aggregation due to Gibbs-Thomson effect
Surface energy is inversely proportional to radius of curvature, leading to anisotropic surface energy for nonspherical particles
High surface energy causes aggregation to achieve lower free energy state
Test hypothesis by coating with surfactant
Aggregation
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Next Steps
• Now fabricating prototype temperature-sensitive nanoparticles (target 150ºC).
• Test their temperature sensitivity.• Test them in core flow.• Test them in 10m and 20m slim tubes.
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2. Characterization of Fracture Properties using Production Data
• Flow through EGS reservoirs is dominated by fractures
• Emphasis on understanding well-to-well relationships
• Focus on the correspondence between tracer and thermal transport
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The Palinpinon Data
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A Synthetic Example
• How hard is it to solve an ideal synthetic example?
25,
1 0
( ) ( )( ) exp44
tr i i i i
pi ii
c t u L uc t dDD
τ ττ
τπ τ=
− −= −
∑∫
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A Synthetic Example
• Ideal synthetic data
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A Synthetic Example
• Regularized blind deconvolution
penaltyroughness
T
misfitdata
pT
p RHcHcF κκκκκ 21
21 )()()( +−−=
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A Synthetic Example
• Solving the ideal case is hard…
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Discrete Fracture Simulation
• Discrete fracture discretization method by Karimi-Fard et al.
• Implemented in TOUGH2 and GPRS
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Discrete Fracture Simulation
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Discrete Fracture Simulation
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Discrete Fracture Simulation
• Tracer and thermal returns for two fracture networks
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Simplified DFN Method
• View the fracture network as a directed graph
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Simplified DFN Method
• Dispersion attributable to the fracture network
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Next Steps
• Characterize the fracture network in terms of its output responses (chemical and thermal)
• Seek “indicators” of network types
• Characterize the EGS in terms of its indicators, and hence forecast its performance
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• Investigate ways use Electrical Resistivity Tomography (ERT) to characterize fractures
• Study resistivity anomalies between electrodes inside geothermal wells to infer fracture properties
• Investigate ways to enhance the contrast between fracture and rock resistivity
• Study the possibility of using conductive fluid
• Explore influences of temperatures and fluid stream and the effects of mineralization
3. Resistivity Tomography
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• Poisson’s equation describes the potential distribution due to a point source of excitation:
• Finite difference method used to approximate the solution
• Successive Over-Relaxation iteration method used to numerically solve the potential distribution
Resistivity Modeling
[ ] ),,( zyxq−=∇∇ φσ
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• Potential distribution for a homogeneous resistivity field• The model gives similar results to the Partial Differential
Equation (PDE) Toolbox in Matlab
Resistivity Model
Resistivity Model PDE Toolbox in Matlab
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• PDE Toolbox in Matlab can not take into account heterogeneity
• Fractures modeled with different resistivity
Resistivity Model
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• Potential field varies with the resistivity pattern and the location of current excitation
Potential Fields
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• Two points in a reservoir used to infer at what angle a straight fracture is placed
• Potential differences between the two points calculated for various angles
Fracture Characterization
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• Different potential difference for most patterns but same difference for fractures that are symmetric to each other with respect to a straight line between the wells
Fracture Characterization
2.35
2.36
2.37
2.38
2.39
2.4
2.41
2.42
2.43
0° 24° 45° 66° 90° 114° 135° 156°
Angle θ
Pot
entia
l diff
eren
ce [V
]
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Conductive Fluid:• Conductive fluid enhances the difference between
fracture and rock resistivity• Potential difference measured at various times, i.e. for
different conductive fluid front locations• Helps in recognizing some of the fracture patterns
Fracture Characterization
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• Various fracture patterns modeled• Same potential difference for some of the patterns• Statistics of potential differences for various realistic
fields could possibly be used to imply a fracture pattern
Fracture Characterization
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• Investigate whether results for more realistic fracture patterns can be used to imply a pattern for a field where the potential difference is known
• Three measurement points instead of two points• Conductive fluid • Influences of temperature on resistivity• Potential changes due to fluid streaming potential• Mineralization
Next Steps