robert porjesz - coimce.com

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Seismic reservoir characterisation

Unconventional reservoir (shale gas)

Robert Porjesz

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Overview

Introduction

Conventional seismic modelling – Reservoir rock physics

– Seismic data for sweet spots

– Sample case history

Unconventional (azimuthal) seismic modelling – Reservoir rock physics



Integration with – Quantitative mineralogy

– Microseismic

Financial impact/concluding thoughts

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Overview

Introduction








– Microseismic


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Introduction

Reservoir modelling/drilling plan, operational decision making

– Without seismic

Actual data

Model data

<25%

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Introduction / objectives

Actual data

Model data

<5%

Reservoir modelling/drilling plan, operational decision making

– With seismic

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Introduction


No seismic

With seismic

But not only seismic

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Shale Gas in the US – Extensive drilling

Since 1997, more than 13,500 gas wells completed in the Barnett shale

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0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Jan-07 Jan-09 Jan-11 Jan-13

Ma

xim

um

ga

s 6

mo

. p

rod

uctio

n (

MC

F)

Date

Horizontal Vertical Directional

The shale learning curve

Barnett Shale Development

Multistage

Completions

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70% of unconventional wells in the U.S.

do not reach their production targets.*

60% of all fracture stages

are ineffective.**

73% of operators say they do not know

enough about the subsurface*

*Source: Welling & Company, 2012 **Source: Hart’s E&P, 2012

Uncertainty

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Shale resources are not homogenous

Shale development is capital-intensive

Lateral wells with multi-stage completions are expensive

Economic success rates are low with current approaches

Stimulation requires a significant amount of water

We can’t drill everywhere

The need for Shale Science

DRILL / COMPLETE SMART

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Shale Play Seismic Characterization in a nutshell

Shale Characterization

How will the fractures propagate ?

Is the rock brittle ? “Brittleness”

Stress

Reservoir Quality

Gas in Place Porosity Saturation TOC

y = -0.061x + 2.6671R² = 0.6511

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

0 2 4 6 8 10 12 14

Bul

k D

ensi

ty (G

RI),

g/c

c

Total Organic Carbon (TOC), wt %

BASIC ROCK PROPERTIES(GRI Method)

All JIP Wells

Johnson Trust 1 #2 (Bossier)

Johnson Trust 1 #2 (Haynesville)

Johnson Trust 1 #2 (Haynesville Lime)

585 Samples

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Overview

Introduction








– Microseismic


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Unconventional reservoir rock physics

What the (seismic driven) rock physics is?

What are the benefits/objectives of applying rock physics analysis for reservoir

modelling?

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Kimmeridge Oil Shale Photo: Dr. Ramues Gallois (2011)

http://www.southampton.ac.uk/~imw/jpg-Kimmeridge/14KM-Tidal-Trap-at-Clavells.jpg

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What are the benefits/objectives of applying rock physics analysis for reservoir

modelling?

Kimmeridge Oil Shale Photo: Dr. Ramues Gallois (2011)

http://www.southampton.ac.uk/~imw/jpg-Kimmeridge/14KM-Tidal-Trap-at-Clavells.jpg

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http://www.kgs.ku.edu/Publications/Oil/primer03.html

Pores / Fluid

Rock Matrix

• Rock frame bulk modulus • Porosity • Fluid saturation • Temperature • Pressure • Share modulus • Density • ….

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vp, vs, r

• Rock frame bulk modulus • Porosity • Fluid saturation • Temperature • Pressure • Share modulus • Density • ….

Rock physics

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Stress

Reservoir Quality


y = -0.061x + 2.6671R² = 0.6511

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

0 2 4 6 8 10 12 14

Bul

k D

ensi

ty (G

RI),

g/c

c



All JIP Wells




585 Samples

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Pre-stack Inversion

)21(3 KEDyn

Stat

Dyn

E

Ek

2

S

P

V

V

22

2

Workflow

• Seismic Data Conditioning • Seismic Interpretation • Well – Seismic Ties • Extract Wavelets • Model Building • Inversion

r

2VP

r

sV

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Pay Probability Map – Haynesville Shale Gas

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Multi Attribute - Production map

HIGH

LOW

HIGH

LOW

HIGH

LOW

HIGH

LOW

WPTOC (mean) Lambda Rho (min)

BRITINDX (max) S-impedance (mean)

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Multi Attribute - Production map

WPTOC (mean)

Lambda Rho (min)

BRITINDX (max)

S-impedance (mean)

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NO

N-E

CO

NO

MIC

Interpolation of Production Values

25

EC

ON

OM

IC

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Calibrated Production Map E

CO

NO

MIC

N

ON

-EC

ON

OM

IC

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Overview

Introduction








– Microseismic


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Stress

Reservoir Quality


y = -0.061x + 2.6671R² = 0.6511

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

0 2 4 6 8 10 12 14

Bul

k D

ensi

ty (G

RI),

g/c

c



All JIP Wells




585 Samples

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13600

13800

14000

14200

14400

14600

14800

15000

0 45 90 135 180

Imp

ed

an

ce

Azimuth

Azimuthal Impedance

P-wave Azimuthal Anisotropy

Azimuth F High

stress

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AVAz methods:

– The near offset Rüger equation

– Azimuthal Fourier coefficients

– Simultaneous elastic inversion of Fourier Coefficients

Slide 30

Fractured media characterisation

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AVAz methods:




Slide 31


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A popular method to perform azimuthal AVO is the near offset approximation (Rüger and Tsvankin, 1997)

Where • R(,f): Data for a given angle of incidence and azimuth f • A: Intercept • Biso: Isotropic gradient • Bani: Anisotropic gradient • fiso : Azimuth of isotropy plane

Bani is often associated to the crack density (Hudson et al., 1981) and fiso is the fracture orientation.

Slide 32

fff 22 sin]sin[),( isoaniiso BBAR

The near offset Rüger equation

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AVAz methods:




Slide 33


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2nd order Fourier coefficients directly

related to the anisotropic gradient Bani

4th order Fourier coefficients provide

additional fracture information

Combination of Fourier coefficients

provide fracture properties

(e.g. weaknesses, compliances) and

unambiguous fracture strike

Azimuthally invariant part contains

both isotropic and fracture properties

(Downton, SEG 2011)

Slide 34

))(4cos())(2cos(),( 420 symsympp rrrR fffff

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f1

1

2

fN

n

2

1

n 2 1 f2

n

…

1

u2 v2 u4 v4

2

u2 v2 u4 v4

n

u2 v2 u4 v4

…

Azimuthal angle stacks Azimuthal Fourier Coefficients

0 50 100 150 200 250 300 350-3

-2

-1

0

1

2

3

4

5x 10

-3

Am

pli

tud

e

Azimuth (degrees)

Amplitude vs. Azimuth (40 degrees)

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AVAz methods:





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SEI of azimuthal angle stacks

δT: tangential weakness

δN: normal weakness

Φsym: symmetry axis

TWT, Ip, Is, ρ

δT, δN, Φsym

Inversion minimizes a three term cost function:

f1

1

fN n

2

1

n

f2

n

… 2 1

2 (Downton and Roure, 2010)

f1 f2 fN …

Data misfit Lateral continuity Prior model

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δT: tangential weakness

δN: normal weakness

Φsym: symmetry axis

g: (Vs/Vp)^2

TWT, g

δT, δN, Φsym

Inversion minimizes a three term cost function:

1

u2 v2 u4 v4

2

u2 v2 u4 v4

n

u2 v2 u4 v4

…

θ1 θ2 θN …

Data misfit Lateral continuity Prior model

(Roure and Downton, 2012)

SEI of Fourier coefficients

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Interpretation Crossplot

Static Young’s Modulus

H- h

H

Zone 1: Ductile (RED) Zone 2: Aligned Fractures (YELLOW) Zone 3: Hydraulic Fractures (GREEN) Zone 4: Transition (GREY)

Dif

fere

nti

al H

ori

zon

tal S

tres

s R

atio

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Plate orientation: direction of H

Combining Stress & Brittleness Seismic Predictions

BRITTLE

Plate size: H

hHDHSR

Young’s Modulus

low

high Hmax

hmin

Hmax

Pressure

hmin = Closure Stress

low high

DHSR

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Predicted average production calibrated to horizontal well length

1 2

3 4

5 6 7

9 10

11

12 13 14

15 16

17

18

20

21

19

22

23 24

25 26

27 28

High

Low

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Overview

Introduction








– Microseismic


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Introduction


No seismic

With seismic


Quantitative mineralogy

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Cuttings Based Spectral Gamma Curve

75 µm 75 µm

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Well log and XRD lithology with up-scaled and normalized RoqSCAN® carbonate data (X’s)

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Vertical Data Summary Log

Imported logs Comments Siliciclastics &

carbonates

Provenance &

marine

indicators Porosity

data Bulk minerals Heavy

minerals

Roq

SC

AN

SG

R

Re

do

x &

org

anic

pro

xie

s

Ro

qF

RA

C

Cuttings Based Spectral Gamma Curve

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Input to seismic attribute mapping and completions characterization

1000 ft

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RoqFRAC® (‘Brittleness’ Index) vs. Dynamic Young’s Modulus

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Introduction


No seismic

With seismic


Microseismic

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Correlation between SRV and Seismic-Derived Attributes

For each stage: 1) Compute SRV 2) Compute average of seismic

attribute inside SRV

Young’s Modulus

SRV

R = 0.68 (76 stages)

DHSR

SRV

R = - 0.60 (76 stages)

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Overview

Introduction








– Microseismic


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Original 13 stages Frac design and calibrated production map

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Targeted, 9 stages Frac design and calibrated production map

Optimized locations for frac stages

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Well Economics

($10.0)

($8.0)

($6.0)

($4.0)

($2.0)

$0.0

$2.0

$4.0

$6.0

$8.0

$10.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Cu

mu

lati

ve C

ash

Flo

w (

$M

M)

Month

Base Case Test Case 1 Test Case 2

Original design

13 stages

9 stage design vs

original 13 stages

Optimized

9 stage design vs

original 13

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Final comments

The shale reservoir characterization workflow demonstrated

utilizes a combination of detailed well analysis, pre-stack

seismic inversion, and seismic anisotropy.

There is no single silver bullet; multi-attribute analysis is

required.

Validation will help refine seismic processes, mineralogy and

microseismic analysis show promising correlations.

Sweet Spot maps and volumes are statistically derived.

Judicious validation should be applied.

robert porjesz - coimce.com

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