Journey to the Centre of the Unconventional Play: The Pathway from Regional Analysis through Quantitative Interpretation to Well Planning September 26, 2012

Presented by:

Cheryl Wright

Neil Watson

Amy Fox

Laurie Bellman

Welcome!

Agenda

• CHERYL WRIGHT

Introduction

• NEIL WATSON

Montney Regional Setting and Production Summary

• AMY FOX

Geomechanics

• LAURIE BELLMAN

Quantitative Interpretation

• KAUSH RAKHIT

Concluding Remarks

Acknowledgements

Next Talk – Cap Rock Integrity issues

Hydrodynamics

Source Rock

Evaluation

Reservoir Characterization

Stress Analysis

Quantitative Seismic

Interpretation

Well Planning

Geomechanics

Montney, the #3 N.A. Resource Play

EIA, 2011

Montney Facies Map

• Detailed understanding of facies through high resolution stratigraphy and core work

Hydrodynamics and Facies Integration

Montney P/D Ratio

Montney Wet Gas Index Map

Montney Isotherm Map

Montney IP Vertical vs. Horizontal

Montney Tight Gas Project

Montney Tight Gas Project Area

Montney Focus Area

Montney Multi-Well Production Chart

MM

CF/

day

Average/Well Production Gas by Quartile

1st Year Ave Production and Forecast Cum. Gas

18 Mo. Cum Prod Vs Technology Group

Montney Quartile Distribution Map

Hydrodynamics

Source Rock

Evaluation

Reservoir Characterization

Stress Analysis

Quantitative Seismic

Interpretation

Well Planning

Geomechanics

Decision Option A

1. Set a rig where access is easy.

2. Drill the same well the guy in the township next to you did and

expect the same results.

3. React to drilling surprises as they occur.

4. Collect the standard, minimum data required.

5. Pay for as many frac stages as you can afford and cross your fingers.

6. Repeat steps 1 through 5.

Decision Option B

1. Start with a field development plan that’s based on a preliminary

understanding of your reservoir from seismic and offset well data.

2. Drill an efficient well, proactively addressing any expected issues.

3. Target your data collection efforts to reduce uncertainty in the most

important reservoir parameters.

4. Plan your completion to take advantage of the geological and

geomechanical setting, avoiding unnecessary frac stages.

5. Use your drilling, well and production data to refine your reservoir

understanding.

6. Repeat steps 1 through 5 with increased efficiency and value.

How can Geomechanics help?

GEOMECHANICS is how in situ stresses, pressures and rock properties affect your decision-making in all stages of the reservoir life cycle

How can Geomechanics help?

Geomechanics

Hydrofrac optimization

Drilling parameters

Thermal operations, EOR

Natural fractures, weak bedding planes Caprock integrity

Fault stability and reactivation

Completion design

Key Geomechanical Parameters

VERTICAL STRESS

• Density logs

• Pseudo-density from sonic

• Average rock densities

MINIMUM HORIZONTAL STRESS

• Leak-off tests

• Minifracs or hydraulic fracturing data

• Lost circulation pressures

HORIZONTAL STRESS DIRECTION

• Wellbore failure observed in image or caliper logs

• Cross-dipole sonic logs

• Regional knowledge/active geologic structures

PORE PRESSURE

• Direct measurements

• Kicks, inflows

• Log- or seismic-based predictions

• Reservoir engineering data

MAXIMUM HORIZONTAL STRESS

• Modeling of wellbore failure/ drilling events

ROCK PROPERTIES

• Tests on core

• Log-based calculations

• Seismic

Heidbach, O., Tingay, M., Barth, A., Reinecker, J., Kurfeß, D. and Müller, B., The World Stress Map database release 2008 doi:10.1594/GFZ.WSM.Rel2008, 2008.

Stress in Alberta

•Kind of, but not really. Uniform?

• It should! Will it affect

how you drill?

• It sure will! Will it affect your fracs?

•Most likely, yes… Will it affect production?

Production from Natural Fractures in Shale

• $60-80K LOG ACQUISITION

& INTERPRETATION

• $300K + FRAC STAGE

• Priceless SMART DECISIONS

Based on SPE 145849 and SPE 1469122

NATURAL FRACTURES PICKED FROM IMAGE LOGS

NATURAL FRACTURE DENSITY

PRODUCTION LOG

FRAC STAGES AND PORTS

Natural Fracture Permeability

• There are no open tensile fractures (mode I) occurring naturally at depth

• Fractures at depth are sliding mode (modes II and III) shear fractures

• Shear fractures that are optimally oriented for frictional failure under in situ or stimulation conditions are the most permeable

Baker Hughes GMI•MohrFracs™

/Sv

n/Sv

= shear stress Sn = normal stress Pp = pore pressure

n = effective normal stress = Sn – Pp

Increase Pp → decrease n → “turn on” more fractures

Data from multiple wells and seismic

Individual well, drilled, logged, analyzed Planned well

Baker Hughes JewelSuite™

Hydrodynamics

Source Rock

Evaluation

Reservoir Characterization

Stress Analysis

Quantitative Seismic

Interpretation

Well Planning

Geomechanics

Depositional Environment Quartz and Carbonate Content Brittleness Pressure Stress Fluid Type Productivity Porosity TOC Etc…

Reservoir Properties Attributes Derivable from Seismic

P-impedance S-impedance Density Young’s Modulus Poisson’s Ratio Lambda*Rho Mu*Rho Etc…

Attribute Correlation

What is QI?

• Conventional seismic interpretation provides geometry.

• Quantitative interpretation tells us about rock properties by rearranging the seismic amplitude values to represent geology.

Single Seismic Attribute Correlates to Microseismic Events

Poisson’s Ratio map with and without microseismic events. Source: Norton et al., 2011, Integration of Surface Seismic and Microseismic for the Characterization of a Shale Gas Reservoir, CSEG Recorder, Jan. 2011, p 31-33.

Single Seismic Attribute Correlates to Closure Stress

Source: Monk et al., 2011, Shale Gas and Geophysical Developments, CSEG Recorder, Jan. 2011, p35-38 Isotropic Closure Stress

Focus on 3 Wells

8-30-82-22

6-10-79-15

11-28-74-10

100/6-10-79-15W6

Density 2400 2800

GR 0 200

Vp

Vs 300

400

100

200

Zp

Zs 5000

5000

15000

10000

LR

MR 0

20

80

100

PR

YM 0

20

0.4

80

Acquired Logs Computed Logs M

ontn

ey F

orm

atio

n 2150

2200

2250

2300

2350

Deterministic Rock Physics Templates (DRPT)

Young’s Modulus vs Poisson’s Ratio

Poisson’s Ratio

Yo

un

g’s

Modulu

s

Relative Brittleness Highlighted

Density

2400 2800

GR 0 200

Vp

Vs 300

400

100

200

Zp

Zs 5000

5000

15000

10000

LR

MR 0

20

80

100

PR

YM 0

20

0.4

80

2150

2200

2250

2300

2350

100/6-10-79-15W6

Shale Play Brittleness Comparison

Matt McKeon – Halliburton

Montney

Relative Brittleness Highlighted

100/6-10-79-15W6

Lambda*Rho

Mu

*R

ho

Poisson’s Ratio

Yo

un

g’s

Modulu

s Lamé Parameters as Proxy for Young’s Modulus vs Poisson’s Ratio

100/6-10-79-15W6

Remember the 3 Wells?

8-30-82-22

6-10-79-15

11-28-74-10

Rock Property Variation Between Wells

Poisson’s Ratio

Yo

un

g’s

Modulu

s

Rock Property Variation Between Wells

Poisson’s Ratio

Yo

un

g’s

Modulu

s

DRPT Process

• QC all wells with dipole sonics

• Compute elastic properties and correlate to reservoir parameters

• Create comprehensive rock physics templates based on deterministic analysis conditioned by regional understanding

Mu

*R

ho

300

325

350

DEPTHMETRES

LMR.RHO_RP_1K/M31750 2750

LMR.RHO_1K/M31750 2750

dt4pUS/M500 100

298wbsk_c_g_tp

299

mcmr_ch_tp300

top_g_bs5

305top_wat_tp

2307

top_wat_bs3

310

max_pay_tp42

353

max_pay_bs3

355

TOPS.TOPS

WIRE.GR_1GAPI0 200

300

325

350

DEPTHMETRES

LMR.LAMBDA_RHO_1GPA-K/M38000 25000

LMR.MU_RHO_1GPA1000 10000

λρ

300

325

350

DEPTHMETRES

LMR.RHO_RP_1K/M31750 2750

LMR.RHO_1K/M31750 2750

dt4pUS/M500 100

298wbsk_c_g_tp

299

mcmr_ch_tp300

top_g_bs5

305top_wat_tp

2307

top_wat_bs3

310

max_pay_tp42

353

max_pay_bs3

355

TOPS.TOPS

WIRE.GR_1GAPI0 200

300

325

350

DEPTHMETRES

LMR.LAMBDA_RHO_1GPA-K/M38000 25000

LMR.MU_RHO_1GPA1000 10000

μρ

Filter: FAC_LMR<7&FAC_LMR<>4&SHEAR_QUAL>0.6&BB_FLAG==1Range: All of Well

Well: 47 Wells

LMR.MU_RHO_1 vs. LMR.LAMBDA_RHO_1 Crossplot

0.1 13.4

Color: Maximum of FAC_LMR

Wells:102062507708W400 102110807707W400 1AA011207708W4001AA012107707W400 1AA020807707W400 1AA023507707W4001AA030207708W400 1AA033407707W400 1AA042107707W4001AA052907707W400 1AA053207707W400 1AA061707707W4001AA062307708W400 1AA063407707W400 1AA070207807W4001AA071807707W400 1AA081507708W400 1AA081707707W4001AA082007707W400 1AA082607707W400 1AA082707707W4001AA082807707W400 1AA082907707W400 1AA083007707W4001AA091807608W400 1AA091807707W400 1AA092007608W4001AA092707608W400 1AA092907608W400 1AA101607608W4001AA101607707W400 1AA102807707W400 1AA103407608W4001AA113507608W400 1AA121707708W400 1AA122007707W4001AA122607708W400 1AA142207707W400 1AA143007707W4001AA151807707W400 1AA152807707W400 1AA152907707W4001AB042107708W400 1AB072207708W400 1AB081407708W4001AB123107707W400 1AB142307708W400

Functions:kinosis_mudcurve_cubic : Regression from curve kinosis_mudcurve

MU = (6657.917 - 1.24686*(x) + 8.84e-05*(x)**2- 1.43184e-09*(x)**3)

10

00

0

11

50

0

13

00

0

14

50

0

16

00

0

17

50

0

19

00

0

20

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0

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00

0

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25

00

0

2000

2600

3200

3800

4400

5000

5600

6200

6800

7400

8000

LM

R.M

U_R

HO

_1 (

)

LMR.LAMBDA_RHO_1 ()

3716

361429

0

24

63

Lambda*Rho

Filter: FAC_LMR<7&SHEAR_QUAL>0.6Range: All of Well

Well: 47 Wells

LMR.MU_RHO_1 vs. LMR.LAMBDA_RHO_1 Crossplot

Wells:102062507708W400 102110807707W400 1AA011207708W4001AA012107707W400 1AA020807707W400 1AA023507707W4001AA030207708W400 1AA033407707W400 1AA042107707W4001AA052907707W400 1AA053207707W400 1AA061707707W4001AA062307708W400 1AA063407707W400 1AA070207807W4001AA071807707W400 1AA081507708W400 1AA081707707W4001AA082007707W400 1AA082607707W400 1AA082707707W4001AA082807707W400 1AA082907707W400 1AA083007707W4001AA091807608W400 1AA091807707W400 1AA092007608W4001AA092707608W400 1AA092907608W400 1AA101607608W4001AA101607707W400 1AA102807707W400 1AA103407608W4001AA113507608W400 1AA121707708W400 1AA122007707W4001AA122607708W400 1AA142207707W400 1AA143007707W4001AA151807707W400 1AA152807707W400 1AA152907707W4001AB042107708W400 1AB072207708W400 1AB081407708W4001AB123107707W400 1AB142307708W400

Functions:kinosis_mudcurve_cubic : Regression from curve kinosis_mudcurve

MU = (6657.917 - 1.24686*(x) + 8.84e-05*(x)**2- 1.43184e-09*(x)**3)

80

00

10

20

0

12

40

0

14

60

0

16

80

0

19

00

0

21

20

0

23

40

0

25

60

0

27

80

0

30

00

0

1000

1900

2800

3700

4600

5500

6400

7300

8200

9100

10000

LM

R.M

U_R

HO

_1 (

)

LMR.LAMBDA_RHO_1 ()

18317

18039183

0

145

99

Mu

*R

ho

Lambda*Rho

DRPT

Seismic Attributes

Seismic Data

Computed Logs

Seismic Attribute

Cross-plots

Classified Volume

QI Summary

Thank You!

Acknowledgements

• Neil Watson – Consulting Services Director

• Laurie Bellman – Geophysics and Quantitative Interpretation Director

• Amy Fox – Senior Geomechanics Specialist

• Cheryl Wright – Client Relations Business Development Director

• Nancy Laing – Client Relations Geoscience Consulting

Acknowledgements

• David Hume – Multi-Client Studies Director

• Kaush Rakhit – President

• Neil Praught – Geological and Geophysical Technologist

• Ally Masoud – Lead Graphic Designer

• Catherine Allen, Pete Singbeil – Introspec Energy Group

• Energy Navigator

• Chris Hicks – Weatherford CWS

• Baker Hughes

Caprock Integrity