seismic reservoir characterization of shales -...
TRANSCRIPT
David Paddock
Scientific Advisor – Seismic Reservoir Characterization
Schlumberger Seismic Reservoir Characterization
Rick Lewis, Schlumberger Unconventional Resources (Petrophysics)
Don Lee and Colin Sayers, Schlumberger DCS Geomechanics
Roberto Suarez, Schlumberger Unconventional Resources Innovation Center
Niranjan Banik and Mark Egan, Schlumberger WesternGeco
Lei Zhang, Schlumberger DCS Seismic Reservoir Characterization
Hari Ramakrishnan, Peter Kaufman, and Brian Toelle, Schlumberger DCS GPE
Joel Le Calvez, Schlumberger DCS StimMAP Microseismic
Richard Salter, Schlumberger DCS North America
,
Seismic Reservoir Characterization of
Shales: An Update
AAPG eSymposium, February 2012
Target
Low High
The basics: Wellbore planning
Fault Delineation
Seismic for the Life of the Asset
Exploration:
– Identify lithofacies for informed pilot well locations
Confirmation
– Reservoir Quality and Completion Quality mapping
– Confirmation well drilling and intial completion trials
– Intial “bucketing” of acreage
• Eventual “factory acreage” – Contains both reservoir quality and completion quality
• Acreage with no potential – Reservoir, kerogen, or maturity lacking
• Challenged acreage – Reservoir, but little or no containment
Questions for defining your unconventional play
1. What should I expect from my acreage?
2. What is the optimized spacing?
3. What science do I need to optimize my capital?
Answers for defining your unconventional play
1. Introduce technologies and workflows that have a
prolonged positive effect on field development and
well spacing.
2. Leverage processes designed for unconventional
resources that can be used to enhance ROI while
reducing risk and building confidence in decision
making.
3. Educate and train personnel for current and future
projects with predictive models that encompass
geomechanics, geophysics, engineering and geology.
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10%
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30%
40%
50%
60%
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80%
90%
100%
No
rmal
ize
d P
rod
uct
ion
Acquisitio
n
Signal DP
and
Imaging
Footprint
suppressio
n, noise
attenuation
VVAZ
Ant
Tracking
Fracture
Corridor
Mapping
AVOAz
Inversion
and
Tensors
Fracture
Area,
Length,
Density,
Aperture
Faults
Discrete
Fracture
Network,
IFM,
Hybrid
Structural
and
Stratigraphic
Modeling,
Facies
Modeling
and
Petrophysic
al
distribution
Simulation
/ History
Matching
Productio
n
Prediction
Fractured Reservoir Characterization and Production Prediction
Attributes
FractureMap
Acquisition Processing Data
Conditioning
Fracture
Attributes
Fracture
Properties
Fracture
Model
Geological
Model
Dynamic
Model
Answers
Frac
candidate
prediction
Well
Ranking
Seismic
Velocity
Analysis
Pore
Pressure
Prediction
Petrophysic
al Analysis
Core and
Image Log
Interpretatio
n
HRA
Borehole
Analysis
Prediction
Scenarios
Geophysics Geology Engineering
Borehole Images
Mud log, mud losses, etc
Sonic Scanner Data,
Single well tests,
Interwell tests
Formation Tests
Production logs
High fidelity and bandwidth,
full/wide azimuth, long offset
seismic data
Shale Success: Reservoir Quality and
Completion Quality (RQ * CQ)
Drill horizontally in a stiff zone coincident with or adjacent to
your primary reservoir
Frac into an interval above (and below) which is stiff and
contains additional reservoir
Contain the frac with argillaceous ductile zones that contain
the frac energy before we get into bad rock
Stiffness stratigraphy (ductile – stiff – ductile) containing
reservoir
We need both Reservoir Quality and Completion Quality – RQ * CQ
Challenges and Solutions
Structure: Imaging
Sweetspot Identification: Inversion, fracture characterization
Hazard Avoidance: AntTracking
Azimuth Optimization: Stress direction prediction
Productivity Prediction: Seismic to simulation
Spacing Optimization: Seismic to simulation
Results:
• Highest ranking production drivers are:
• Natural fractures and thickness
• Completion quality and porosity
• Acreage can be high-graded as:
• Predicted well performance
• OOIP
• Rock quality distribution
Eagle Ford example: Production driver determination
Shale Limestone
Fractures
Natural fracture PERM, md
Net pay, feet
Hydraulic fracture PERM, md
Primary porosity, percent
Initial pressure, psi/ft
Matrix PERM, nd
Ref Case
10
2/9/2012 Seismic Ant Track time slice
What can I expect from my acreage?
Success is driven by reservoir and completions.
Reservoir quality
•Fractures
•Porosity
•Pore pressure
Completion quality
•Stress
•Stiffness
•Hardness
•Hazard avoidance
Suggested Solutions
Success is driven by reservoir and completions.
Reservoir quality
•Fractures • Azimuthal Inversion
• Prediction of open vs closed
fractures
•Porosity • Pre-stack inversion
•Pore pressure • Surface consistent vel analysis
Completion quality
•Stress • Pre-stack inversion
• Curvature analysis
• Pore pressure estimation
• Closure stress estimation
•Stiffness
•Hazard avoidance • Ant Tracking
Iain Bush
Fracture Characterization
Fracture prediction
•Ant Tracking
•AVOAz inversion
•Velocity versus azimuth
•FractureMAP in gas shales
©2010 WesternGeco
Zss/Zsf
Fracture prediction
•Ant Tracking
•AVOAz inversion
•Velocity versus azimuth
•FractureMAP in gas shales
©2010 WesternGeco
Zss/Zsf
Ants
FCM
AVOaz
Attributes
AVOA Tensors
Fracture
Area,
Length,
Density,
Aperture
Faults
DFN, IFM,
Hybrid
Simulation /
History
Matching
VVAZ
Foot print
suppression,
noise
attenuation Signal DP
and Imaging
e.g. Depth
Acquisition
e.g. WAZ
Conditioning
Post-stack,
pre-stack
DP
2D/3D
Borehole Images
Mud log, mud losses, etc
Sonic Scanner Data,
Single well tests,
Interwell tests
Formation Tests
Production logs
Acquisition Processing Data
Conditioning
Fracture
Attributes
Fracture
Properties Fracture
Model Simulation
FractureMap
Hay, Paddock
Fracture Characterization Seismic Solution
Fracture prediction
follow-on products
•3D Mechanical Earth
Modeling
•DFN
•Prediction of closure
pressure
•Prediction of open versus
closed fractures
©2010 WesternGeco
Zss/Zsf
Porosity prediction
• Pre- or post-stack inversion
• Pre-stack in
• Marcellus
• Haynesville
• Fayetteville
• Post-stack in
• Delaware Basin
• Eagle Ford?
Calcareous
Siliceous
Argillaceous
Marcellus Haynesville Barnett
no favorable depositional
facies for development of a
siliceous matrix
Non Reservoir
Non Reservoir Non Reservoir
Non Reservoir Reservoir
Reservoir Reservoir
Reservoir
SEM images
Target
Low High
Porosity prediction Interplay of rock facies, reservoir quality, and completion quality
Pore Pressure Prediction
• Seismic velocity analysis
• Reflector-by-reflector
• Every trace
• Stability
• Vertical resolution
Surface-Consistent
Velocity Analysis
refined velocity picks
Surface-Consistent
Velocity Analysis
interval velocity
SCVA Vi
transformed to
pore pressure
• Stiffness
• PR and YM
• Hardness
Stress, Stiffness, and
Hardness Prediction
• Stress prediction
• Stiffness cubes from
pre-stack inversion
• Curvature analysis
• Calibrated using
SonicScanner
What Works (So Far)
Fayetteville Shale
Bakken Formation
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Delaware Sands
Wolfcamp Shale
Mancos Shale
Barnett Shale
Marcellus Shale
Marcellus
What Works (So Far)
Fayetteville Shale
Wolfcamp Shale
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Mancos Shale
Niobrara
Delaware Sands
Barnett Shale
Marcellus Shale
Marcellus
Fayetteville Shale
Poisson’s or Vp/Vs ratio for predicting porosity
– Actually predicts the lithofacies that houses the porosity
AntTracking for faults and major fracture swarms
3D Mechanical Earth Model for fractures
– Pre-existing or induced? Or both?
Case Study:
– Geocellular and geomechanical model taken to simulation
– Dual porosity model
– Productivity is correctly predicted for 75% of wells
– History matched with as little as six months of production
• Reservoir engineers report that this is not typical
Southwestern Energy hired away Devon’s AntTrack expert
Understanding and Predicting Fayetteville Shale Gas
Production Through Integrated Seismic-to-Simulation
Reservoir Characterization Workflow
SPE 147226 Hariharan Ramakrishnan, Eva Peza, Shekhar Sinha, Miriam Woods, Christopher Ikeocha,
Flemming Mengel, Yves Simon, Paul Pearce, Jeff Kiester, Steven McKetta and John
Jeffers
November 1, 2011
Hyd. Fracture parameters
• Fracture half-length – 250 – 400 ft
• Fracture conductivity – 5 md-ft
• Fracture complexity – Small fracture
complexity around primary fracture
(Natural fracture swarms in FL2)
• Well landed in FL2
Production Forecast Results
Key Conclusions
Numerical reservoir simulation using a dual porosity model adequately describes
the production behavior of hydraulically fractured wells in the study area
Quality of data (log, seismic, core, completion etc.) more important than the
producing time for predictive reservoir model
Reservoir Quality is strongly influenced by effective gas filled porosity
Upper Fayetteville is not always covered by hydraulic fracturing
Integration of log-core-seismic data critical to understand the reservoir and to
predict production
Value – Lateral Placement and Stimulation
Optimization
Total Organic Carbon (TOC) Fracture
Permeability
Stress
Gradient
Matrix
Porosity
A AA’ Distance ~ 5.7mi
A’
What Works (So Far)
Fayetteville Shale
Bakken Formation
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Delaware Sands
Wolfcamp Shale
Mancos Shale
Barnett Shale
Marcellus Shale
Marcellus
Bakken Formation
Reservoir quality important
Thickness and/or subcrop important
Open fractures thought to be important
Have differentiated good versus bad wells using pre-stack inversion
Have delineated geobodies in the Middle Bakken
Case Study:
– Phase 1
• Post stack inversion, AntTracking, and curvature
– Phase 2
• Azimuthal pre-stack inversion and fracture tensor analysis (patented)
• Stochastic pre-stack inversion
What Works (So Far)
Fayetteville Shale
Wolfcamp Shale
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Mancos Shale
Niobrara
Delaware Sands
Barnett Shale
Marcellus Shale
Marcellus
Haynesville Shale
Fractures very important in some areas
– Examples where the only microseismic events are on small faults
Containment variations important in some areas
Pore pressure variations important in some areas
Small faults an annoyance
– AntTracking for fault delineation
Case Study 1:
– 3-fold variability in water production
– Moderately successful in explaining
– Ants as fracture proxy
Case Study 2:
– Seismic used to explain variations in containment, which is a primary driver
No data release
What Works (So Far)
Fayetteville Shale
Wolfcamp Shale
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Mancos Shale
Niobrara
Delaware Sands
Barnett Shale
Marcellus Shale
Marcellus
Woodford Shale
FractureMAP has been effective
Case Study:
– 19-fold variability in well results
• Vertical wells
– Looking for economic well locations
– FractureMAP explained variability
No data release
What Works (So Far)
Fayetteville Shale
Wolfcamp Shale
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Mancos Shale
Niobrara
Delaware Sands
Barnett Shale
Marcellus Shale
Marcellus
Eagle Ford Shale
Fractures are key: AntTracking and azimuthal analysis
Formation thickness
Reservoir quality varies slowly.
– Clients claim that acoustic impedance can map reservoir quality
Overlooked faults can bring H2S from below
Case Studies:
– AntTracking only seismically
– Geology / production analysis finds that fractures, thickness, and porosity are the
primary drivers. HiWay increases production by 30%.
AntTrack example just south of Eagle
Ford trend in Karnes, Goliad, and Dewitt
Counties
What Works (So Far)
Fayetteville Shale
Wolfcamp Shale
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Mancos Shale
Niobrara
Delaware Sands
Barnett Shale
Marcellus Shale
Marcellus
Avalon Shale
A multiclient project underway
– Acoustic impedance adequate for
reservoir quality
– Would still want PR and YM for
completions (may be done)
A project underway associated with
consortium work but proprietary
Gather conditioning on legacy data
is a hurdle / enabler
Completion quality evaluation
– Some clients say no need
– Others want to identify a frac attribute
Oil and Gas Journal map
What Works (So Far)
Fayetteville Shale
Wolfcamp Shale
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Mancos Shale
Niobrara
Delaware Sands
Barnett Shale
Marcellus Shale
Marcellus
Delaware Sands
Combined azimuthal pre-stack
inversion and AntTracking
Case Studies:
– Azimuthal pre-stack inversion
• Difficult to extract, despite being
modern conventional data
– AntTracking
– Combined analysis explanatory
Density inversion best for
identifying good versus bad
areas
– Adequacy of AI was not
evaluated
Oil and Gas Journal map
What Works (So Far)
Fayetteville Shale
Wolfcamp Shale
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Mancos Shale
Niobrara
Delaware Sands
Barnett Shale
Marcellus Shale
Marcellus
Wolfcamp Shale
Open fractures very important
FractureMAP and AVO Az
inversionidentify sweetspots
AVO Az inversion easily extracted from
single sensor data (anecdotal or
diagnostic?)
AntTrack: correlated with production
variations
Case Study:
– 30-fold variability in well results
– Needed a way to predict results
– Difficult seismic data area
What Works (So Far)
Fayetteville Shale
Wolfcamp Shale
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Mancos Shale
Niobrara
Delaware Sands
Barnett Shale
Marcellus Shale
Marcellus
Mancos Shale
Drivers unknown
Case study
– Core / log / seismic classification study underway
What Works (So Far)
Fayetteville Shale
Wolfcamp Shale
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Mancos Shale
Niobrara
Delaware Sands
Barnett Shale
Marcellus Shale
Marcellus
Niobrara
Fractures are key
– AntTracking
– Azimuthal velocity analysis and
azimuthal pre-stack inversion
Adequate maturity / pore pressure is
required
– Trace-by-trace, reflector-by-reflector
velocity analysis
Case study
– None
Jarvie 2011
What Works (So Far)
Fayetteville Shale
Wolfcamp Shale
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Mancos Shale
Niobrara
Delaware Sands
Barnett Shale
Marcellus Shale
Marcellus
Barnett Shale
Hazard avoidance important: AntTracking
Reservoir and completion quality: pre-stack inversion
– Porosity prediction works best by predicting the lithofacies that holds it
– Variations within townships
– Stiffness stratigraphy and lower containment drive completion quality
Case Study:
– Core analysis
– Cluster classification of triple combo well logs: 20 facies identified and correlated
– Seismic pre-stack inversion
– Combined LithoCube / Markov Sequence Stratigraphic seismic classification
• Five key facies identified
– Not yet released
What Works (So Far)
Fayetteville Shale
Wolfcamp Shale
Haynesville Shale
Woodford Shale
Eagle Ford Shale
Avalon Shale
Mancos Shale
Niobrara
Delaware Sands
Barnett Shale
Marcellus Shale
Marcellus
Marcellus Shale
Previous projects
Reservoir quality impacts production more than does completion
quality by a factor of 3:1.
Young’s modulus: good wells versus bad, but not porosity
– Soft is good
Compressional impedance: Vkerogen
– Can successfully predict good versus bad acreage, but YM is better
Microseismic events most correlative with Poisson’s ratio if no faults
AntTrack features can steal frac fluid or be the prize
Case Study:
– Client ready to abandon 25 square mile area after several wells
– Breakeven or worse
– Able to discern good versus bad acreage, confirmed by drilling.
– Could have doubled their money if they had used SRC throughout program
Acreage Ranking in the Marcellus Shale:
Integrating LWD EcoScope and
SonicVision,
with 3D Seismic
Peter Kaufman1, Keith Atwood2, Gary Forrest1, Kirby Walker3,
Kevin Wutherich3, Babatunde Ajayi3, Denise Delozier3, Alex
Perakis1, Shannon Borchardt1, Ken Hauser1
(1Denver, 2Houston, 3Pittsburgh)
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90%
100%
No
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ize
d P
rod
uct
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Production Statistics:
Production Rates 40-Year EUR
Cum Gas Produced (MCF)
Maximum Single Daily Rate (MCF)
30 Day Cum (MCF)
60 Day Cum (MCF)
90 Day Cum (MCF)
120 Day Cum (MCF)
MaxOfRunning 30Day Cum
MaxOfRunning 60Day Cum
MaxOfRunning 90Day Cum
MaxOfRunning 120Day Cum
Production Metrics (up to 28)
Pro
du
ctio
n M
etri
cs
> 30% of best well < 30% of best well
Compilation of Potential Production Drivers
Petrel
DECIDE!
Effective Porosity (PIGN)
Total Porosity (PIGT)
Water Saturation (SUWI)
TOC (WSM1)
Compressional Slowness (DTCO)
Shear Slowness (DTSM)
Poisson's Ratio (PR)
RT
NPHU
RHOZ
SIGMA
Aluminum (DWAL WALK2)
Calcium (DWCA WALK2)
Iron (DWFE WALK2)
Silicon (DWSI WALK2)
Sulfur (DWSU WALK2)
Son
icT
rip
le C
om
bo
Nu
cle
ar
Spe
ctro
sco
py
Geologic Parameters
Pe
tro
ph
ysic
s
Marcellus Isochron
Cherry Valley Amplitude
LM Neg Amplitude
Onondaga Amplitude
Seismic Ant Avg
Max Pos Curvature
Seismic Parameters
Seis
mic
Att
rib
ute
s
Total Lateral Length (TMD)
Depth Change Landing to TD
Well TVDSS (Landing point)
Total Lower Marcellus
Zone 4
Sweet Zone
Zone 1&2
LM High GR Zone
Upper Marcellus
Sweet Zone and Zone 1&2
%Sweet
%Sweet and Zone 1/2
%LM high GR
%UM
Drilling Parameters
We
ll
Ge
om
etr
y
Frac
tio
n in
Zon
eLe
ngt
h in
Zo
ne
40-Year EUR
Cum Gas Produced (MCF)
Maximum Single Daily Rate (MCF)
30 Day Cum (MCF)
60 Day Cum (MCF)
90 Day Cum (MCF)
120 Day Cum (MCF)
MaxOfRunning 30Day Cum
MaxOfRunning 60Day Cum
MaxOfRunning 90Day Cum
MaxOfRunning 120Day Cum
Production Metrics
Pro
du
ctio
n M
etri
cs
# Stages
Average Length per Stage
# Perforation Clusters per Stage
Cluster Length
Perforation shots per foot
100 Mesh M#
40/70 M#
30/50 M#
20/40 M#
Max PPA Achieved
# Prop per Ft Lat
Prop M# Category
Fluid Type
Avg Rate
Max Rate
Breakdown Pressure
Pre-job ISIP
Pre-FG
Post-frac ISIP
Post FG
Net Pressure
HHP
Gel
Total Slurry
Co
mp
leti
on
Par
ame
ters
Completion Parameters
•Reservoir quality has the greatest impact on production
•Correlation with several different log measurements
•Correlation with several different seismic attributes
•Areal variability in reservoir quality dominates operational
decisions (completion, lateral placement zone)
•Three times as much impact as completion variables
•Dominance of reservoir quality necessitates
normalization to quantify impact of operational practices
Production Impacted by Completion Practices Data indicates that reservoir quality
has 3x impact of completion quality • Normalize by (effective porosity)2
Varying job sizes, lateral lengths, and
well placement impact simulated
reservoir volume • Normalize by ESV, determined from
hydraulic fracture modeling calibrated
with StimMap results
Better production correlates with: • Shorter stage lengths
• Higher rates per cluster
• Greater sand volume
• Lower ISIP
• Days shut in before production – i.e.,
reservoir “seasoning”
Probability Green or Cyan Seismic Facies
Marcellus Treasure Map with Well Results
Not yet completed Best
Worst
Marcellus Shale
Previous projects
Reservoir quality impacts production more than does completion
quality by a factor of 3:1.
Young’s modulus: good wells versus bad, but not porosity
– Soft is good
Compressional impedance: Vkerogen
– Can successfully predict good versus bad acreage, but YM is better
Microseismic events most correlative with Poisson’s ratio if no faults
AntTrack features can steal frac fluid or be the prize
Can drive finding costs down by 41% by leaving 68% of acreage fallow
– 60/40 rule: 60% of production from 40% of the acreage. 13% error rate.
Can drive finding costs down by 31% by leaving 50% of acreage fallow
– 75/50 rule: 75% of production on 50% of the acreage. 13% error rate.
Conclusions
Seismic reservoir characterization works
Productivity prediction on several seismic to simulation projects
Production drivers vary from shale to shale
Fractures and pore pressure in the last year have been found to be surprisingly important
Wells in seismic sweetspots typically have 95% higher EUR
Can make the difference between breakeven and doubling your money in an acreage position
Thank You. Questions?
David Paddock
Scientific Advisor – Seismic Reservoir Characterization
Schlumberger Seismic Reservoir Characterization
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