seismic reservoir characterization of shales -...

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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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

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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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




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)





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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seismic reservoir characterization of shales -...

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