CBM Field development planning studies ;

workflows and tools, best practices

Illustrated examples – actual figures have been altered

to ensure confidentiality

Laurent Alessio Managing Partner

Leap Energy Subsurface Consulting Services

Agenda

1. Preamble : CBM plays and the variability complexity Key aspects to frame the problem

Viewing the CBM plays through a statistical lense

Contrasting conventional offshore reservoirs to CBM plays

2. Reservoir Characterisation Key parameters impacting the performance of CBM wells

A few illustrated expected relationships and trends

3. Property Modelling – spatial distribution Geo-statistical methods as a fundamental tool to represent variability

NetCoal and Permeability high-grading

4. Production Forecasting Different methods and associated tools

5. Concept Selection Key elements of the field development planning workflow

Illustration of economical screening – what can be done

6. Conclusions

General characteristics of CBM/CSG

provinces

• Large areas, multiple seams, very variable reservoir characteristics

within short-distances

• Significant inter-well variability, deliverability (perm) and recovery follow

lognormal distribution

Source : SPE 107308

G.Swindell (2007)

Understand Value Of Information – what confidence can be given to sparse data

Concept select decisions under variability

Averaging (upscaling) into a single curve

Source : AAPG 2006

C. Boyer

Viewing CBM provinces through a statistical

lense

Ideal setting for combined geostatistical workflow (Monte-Carlo and

mapping)… but don’t believe the resulting map !

Beware of representativeness of data especially in sparse dataset

Mapping – deterministic vs. statistical representation

Some techniques available for

characterising uncertainty from variability

and available data to date

Confidence curve –SPE-133518

+ =

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60%

% C

on

fid

en

ce

in

Fie

ld M

ea

n

% Data available

Plot 1: Evolution of Confidence Curves (varying folds) vs. % data available

1.10

1.25

2.00

3.00

5.00

7.00

9.00

10.00

88% confidence in a fold of 2.00

At 10% data available (% well drilled)

40% confidence in a fold of 1,25

At 10% data available (% well drilled)

Fold

Asset-Specific Well data

Regional/analogues correlations

*

MeanTopDp[m]

MeanGrossThk[m]

MeanMidDp[m]

MeanNetPay[m]

P10 VL [m3/ton]

P50 VL [m3/ton]

P90 VL [m3/ton]

Mean VL [m3/ton]

P10 PL [kPaa]

P50 PL [kPaa]

P90 PL [kPaa]

Mean PL [kPaa]

P10 DAFGC[g/cm3]

P50 DAFGC[g/cm3]

P90 DAFGC[g/cm3]

MeanDAFGC[g/cm3]

Perm P10 [mD]

Perm P50 [mD]

Perm P90 [mD]

PermMean[mD]

Well data

45.3

293.5

192.1

18.7

28.1

24.3

17.9

23.9

5396.2

3854.4

2312.7

3854.4

6.8

3.3

1.6

3.8

645.19

74.20

8.53

234.79

User Value

45.3

293.5

192.1

14.5

28.1

24.3

17.6

23.9

5396.1

3854.4

2312.6

3854.4

6.8

3.3

1.6

3.8

645

74

8.5

235

Regional&area speci fic

200.0

200

300.0

10

29.3

25.2

18.1

24.8

5673.9

4052.8

2431.7

4052.8

6.8

3.3

1.6

3.8

350.0

132.3

50.0

171.8

MeanTopDp[m]

MeanGrossThk[m]

MeanMidDp[m]

MeanNetPay[m]

P10 VL [m3/ton]

P50 VL [m3/ton]

P90 VL [m3/ton]

Mean VL [m3/ton]

P10 PL [kPaa]

P50 PL [kPaa]

P90 PL [kPaa]

Mean PL [kPaa]

P10 DAFGC[g/cm3]

P50 DAFGC[g/cm3]

P90 DAFGC[g/cm3]

MeanDAFGC[g/cm3]

Perm P10 [mD]

Perm P50 [mD]

Perm P90 [mD]

PermMean[mD]0%

20%

40%

60%

80%

100%

0.1 10 1000

Cu

mu

lativ

e

pro

ba

bil

ity

of

Ex

ce

ed

en

ce

Permeability [mD]

Perm

Cumulative probability

0%

20%

40%

60%

80%

100%

0 5 10 15

Cu

mu

lativ

e

pro

ba

bil

ity

of

Ex

ce

ed

en

ce

Gas Content [m3/t]

GC(scc/g)

Cumulative probability

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1.00E-03 1.00E-02 1.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03 1.00E+04

Cu

m P

rob

ab

ilit

y

Permeability (mD)

Permeability Distribution vs. DepthAssuming NetStress Exponential factors of (P90, P10)=-0.6 , -0.2 Mpa-1

K (mD) at 200 m

K (mD) at 400 m

K (mD) at 600 m

K (mD) at 800 m

K (mD) at 1000 m

K (mD) at 1200 m

K (mD) at 1400 m

K (mD) at 1600 m

K (mD) at 1800 m

K (mD) at 2000 m

0

2

4

6

8

10

12

14

16

18

20

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Gas

Co

nte

nt

(m3/

t)

Depth_mBD

Gas Content vs Depth (Reference Case)

Wallon Bandanna Early Permian Wallon REF Bandanna REF EP REF

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

0 10 20 30 40 50 60 70 80 90 100

pL

(kP

a)

Temp (C)

pL (Langmuir Pressure) vs Temp - REFERENCE CASE

Wallon Bandana EP Wallon Bandanna EP

0

2

4

6

8

10

12

14

16

18

20

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

Gas

Ad

sorb

ed

DA

F (m

3/t

)

Pressure (kPa)

Early Permian Isotherm Model

Ref Sat Hi Sat Lo Sat Gc Ref DB3-C1 DB3-C2 DB3-C3 DB3-C4 DB3-C5 DB3-C6 DB1-CBM455

Projected

Bridge Creek Eurombah East

Currawong

Depth Structure map – Base

ATP

0 5 10 15

Incre

asin

g L

ike

lih

oo

d

>>

Gas Content [m3/t]

X Value

P50

Mean

0%

20%

40%

60%

80%

100%

0 100 200 300 400 500

Cu

mu

lativ

e

pro

ba

bil

ity

of

Ex

ce

ed

en

ce

Gross Res Thickness [m]

Gross Thk

Cumulative probability

0%

20%

40%

60%

80%

100%

0 50 100 150 200

Cu

mu

lativ

e

pro

ba

bil

ity

of

Ex

ce

ed

en

ce

Top Res Depth [m]

Top Coal

Cumulative probability

Spacing [km]

0.88

De

pth

[m b

elo

w s

urf

ace

]

min BHP [psia]

40

Pinnette Horiz

Multi-

latera

l

Deviated Slanted Cavitation Frac UnderReam

No No No Yes Yes No Yes Yes

*Fekete F.A.S.T. CBM

• Variability is defined as a short to medium scale (up to inter-well scale) variations of a given parameter, such as

permeability, porosity, gas content (for CBM reservoirs), hydrocarbon saturation etc… These variations can be often extreme, with

several orders of magnitude differences in permeability commonly observed in fractured reservoirs. Variability is intrinsic and non-

temporal, which means it that does neither change with time nor with the number of data points, and it is a characteristic of the reservoir

(for a given sampling scale*). Ultimate understanding of variability often remains spatially poorly predictive, so the authors recommend a

statistical approach is always conducted in parallel.

• Uncertainty is defined at the field scale, or at least, a sector or segment of the field (field unit), where multiple

wells will be ultimately drilled. It represents, at a given time, how well a field unit is understood. Generally, uncertainty reduces

with time and information becoming available, provided the right framing and uncertainty assessment was conducted. Arguably, the

uncertainty in subsurface givens, such as field porosity average, or in place volumes is strictly a consequence of our lack of knowledge.

Development related metrics, such as field recoverable volumes or production performance, at a given time, are a consequence of our

level (or lack of) of understanding of the subsurface and the concept development choices we have made and will be making.

Reference : SPE 133518

Variability vs Uncertainty

Field Uncertainty

K(mean) vs. depth (P90, P50, P10)

P10

P90

Variability

K(well) vs. depth

OPERATE IMPLEMENT DEFINE CONCEPT SELECT

CONCEPT ASSESSMENT

PROJECT INITIATE

TRADITIONAL E&P OPPORTUNITY MATURATION CAN BE USED BUT ITERATIVELY THROUGH PROJECT LIFE

UNCERTAINTY REDUCTION VS. TIME AND GATE

% SCOPE IMPLEMENTED VS TIME AND GATE

----- CSG : Early pilots and Tech

trials provide some de-risking,

but progressive Uncertainty

Reduction through continuous

development

- - - Conventional:

Major uncertainty

reduction through

Appraisal, FDP studies

and Development drilling

Uncertainty reduction is more progressive through different

project phases in CBM/CSG plays

Residual uncertainty remains through to OPERATE

50%

100%

Time, asset maturation

Success in CBM Development Planning :

Integration of disciplines and reservoir data to achieve a distribution

shift of outcomes

0

1

2

3

4

5

6

7

8

Well PotentialProbability Curve

Optimised

development

plan

Outcome improvement Economic/Value-Metric

Increase the probability of

drilling higher deliverability

wells

Increase the probability of

drilling higher GIP/km2

wells

Pure

pattern drill

Geophysical Geological Petrophysical Reservoir Eng.

Structural deformation

Faults

Coal wash-outs

High net coal mapping

Fracture interpretation

from core

Structural history

Tectonic history

Borehole image

Breakouts and fracture

interpretation

DST interpretation

Formation testing

RESERVOIR CHARACTERISATION

Uncertainty Assessment process

Identify

Uncertainties

Quantify

ranges

Assess

impactRank

Uncertainty assessment workflow

ANOVA

Tornado Plotting

Analogue / Data

assessment

Key Parameters impacting Recoverable Volumes

and some ‘to be expected’ relationships

RECOVERABLE UNCERTAINTY

-3.1%

1%

0%

5%

3%

2%

-2%

0%

-7%

-5%

-30.0% -20.0% -10.0% 0.0% 10.0% 20.0% 30.0%

GC

Skin

Net

Perm

DI

Change to Recovery Factor

Sector 'A'

Hi

Low

-4%

3%

0%

13%

6%

3%

-3%

0%

-17%

-8%

-30% -20% -10% 0% 10% 20% 30%

GC

Skin

Net

Perm

DI

Change to Recovery Factor

Sector 'B'

Hi

Low

Permeability vs depth

A proposed distribution per depth bin model – in line with a prediction of decreasing K

vs. Net Stress and nature of stochastic distribution (log-normal)

DST MEASURED PERMEABILITY FITTED LOG-NORMAL DISTRIBUTIONS, DEPTH-BINNED

P10

P90

Match obtained with P90 and P10

follow a different degradation path

Use an exponential decline vs. net

stress (depth)

Isothermal properties

Dewatering Index as a key measure of performance

PROPOSED DEFINITION DEWATERING INDEX

0

100

200

300

400

500

600

700

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Gas

Co

nte

nt

(scf

/to

n)

Pressure (psia)

Langmuir Isotherm

Langmuir Isotherm

Initial Condition

Critical Point

Abandonment Point

a

b

Dewatering Index is designed to

characterise best the production

response of a well :

The distance between Critical

Desorption Pressure (CDP) and

Original reservoir pressure (Pi)

drives the dewatering phase.

The gas content difference

between CDP and Pab drives the

recovery and production

performance.

Pi

CDP

Pab

COMPARISON OF IMPACT OF WELL TYPE CURVE PARAMETERS : SATURATION VS. ISOTHERM

Stronger correlation between production parameters and DI compared to Sat %

Isothermal properties

Dewatering Index as a key measure of performance

y = 101.68x2 - 304.25x + 199.39R² = 0.9982

0

20

40

60

80

100

120

140

160

180

200

0 0.2 0.4 0.6 0.8 1 1.2

Pe

ak G

as R

ate

(Msc

fd)

Dewatering Index

Peak Gas Rate vs DI

y = 304.1x2 - 149.15x + 27.068R² = 0.9717

0

20

40

60

80

100

120

140

160

180

200

0 0.2 0.4 0.6 0.8 1 1.2

Pea

k G

as R

ate

(Msc

fd)

Saturation

Peak Gas Rate vs Saturation

y = -6E+08x2 + 1E+08x + 4E+08R² = 0.9787

0.00E+00

5.00E+07

1.00E+08

1.50E+08

2.00E+08

2.50E+08

3.00E+08

3.50E+08

4.00E+08

4.50E+08

5.00E+08

0 0.2 0.4 0.6 0.8 1 1.2

Cu

mu

lati

ve G

as (

scf)

Dewatering Index

Cumulative Gas vs DI

y = -8E+07x2 + 6E+08x - 5E+07R² = 0.9375

0.00E+00

5.00E+07

1.00E+08

1.50E+08

2.00E+08

2.50E+08

3.00E+08

3.50E+08

4.00E+08

4.50E+08

5.00E+08

0 0.2 0.4 0.6 0.8 1 1.2

Cu

mu

lati

veG

as (

scf)

Saturation

Cumulative Gas vs Saturation

Isothermal properties

Dewatering Index as a key measure of performance

COMPARISON OF IMPACT OF WELL TYPE CURVE PARAMETERS : SATURATION VS. ISOTHERM

Stronger correlation between production parameters and DI compared to Sat %

y = 283.16e3.2536x

R² = 0.9209

0

2000

4000

6000

8000

10000

12000

0 0.2 0.4 0.6 0.8 1 1.2

Tim

e t

o P

eak

Gas

(d

ays)

Dewatering Index

Time to Peak Gas vs DI

y = 14805e-3.488x

R² = 0.9702

0

2000

4000

6000

8000

10000

12000

0 0.2 0.4 0.6 0.8 1 1.2

Tim

e t

o P

eak

Gas

(d

ays)

Saturation

Time to Peak Gas vs Saturation

y = -0.7366x2 + 0.2783x + 0.4608R² = 0.9209

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

0 0.2 0.4 0.6 0.8 1 1.2

Re

cove

ry F

act

or

Dewatering Index

Recovery Factor vs DI

y = -0.2396x2 + 0.8068x - 0.0547R² = 0.8446

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

0 0.2 0.4 0.6 0.8 1 1.2

Re

cove

ry F

act

or

Saturation

Recovery Factor vs Saturation

PROPERTY MODELLING

RESERVOIR

PROPERTY

Modelling key geological parameters

Proposed considerations – example NET COAL

LARGE SCALE

TRENDS

UNDERLYING

GEOLOGY

CAPTURE VARIABILITY

Example: Net Coal distribution over large play

Decreasing Net Coal from NE to SW

Variability accounted

for with SGS

Geo-statistical model

Large scale-predictive

Variability represented, but cannot be deterministically trusted at inter- well scale

GAS

CONTENT

Proposed workflow for NetCoal prediction from 2D seismic

A tested method using A/B amplitude techniques to utilise different seismic

vintages

Net coal map based on well

gridding only Geophysically

constrained net

coal mapping

Follow consistent

workflow to

remove amplitude

differences

between seismic

vintages

Net coal map integrating

seismic information

Top reservoir = prominent coal package

Channel Washout (Strong top-coal Peak disappears)

Evidence from

seismic data that

coal wash-outs are

present and can be

detected

Key elements of the fracture network

characteristics and optimisation opportunities

Orientation What are the principal fracture

families and their major

orientation

Density What are the areas where a

significantly different fracture

density occurs

Anisotropy Non-regular spacing orientation – reducing

total drilling costs

Deviated and horizontal well azimuth

Permeability modulus Expected higher ‘average’ deliverability &

dewatering profile

Impact on spacing decisions and well type

The higher # of joints

and cleats intersected by

a well, the higher the

chance that at least one of

these fractures has

enough aperture and

continuity to provide good

permeability

Permeability prediction can be enhanced by mapping of fracture characteristics

Permeability controlled (statistically) by

fracture intensity

Hermitage North 1

Fracture density mapping – expected relationship to

permeability (at least statistical!)

Fracture families are individually mapped and a modulus computed

Combined cleat spacing

(defects/10m) map:

•Based on kriging of well data

guided by cleat orientation

•Each cleat set is gridded

separately first, maps per set

are then merged

•# at wells show the well

observed cleat density

Note: cleat mapping is limited to

areas within 10km from well

control (scanner data)

High fracture density

Low fracture density

Single or multiple directions

Expected to coincide with areas of

higher incidence of high permeability

Single or multiple directions

Expected to coincide with areas of

low incidence of high permeability

• Combined cleat anisotropy map (ratio of

largest over smallest set spacing):

• Based on kriging of well data guided by

cleat orientation

• Each cleat set is gridded separately first,

maps per set are then merged

Mapping Anisotropy – relative density of fracture families

allows the assessment of spacing – quantitative analysis is

possible through reservoir simulation or analytical calculations

Highly

anisotropic

(=unidirectional)

network

Isotropic

network

Anisotropic

gridding

Isotropic gridding

PRODUCTION FORECASTING

• Different methods are possible and in use within the industry

– Analytical and Numerical – objectives should be complementary

– Recommend scaled-approach and understand/capture areal trends

• Key is a realistic property mapping

– Again understand trends and dependencies

• Focus on understanding variability and uncertainty

– Speed and replicability of workflows and tools pays off

Dynamic Modelling Approaches Methods, focus areas and recommendations

Full numerical or material-balance analytical models ? Depending on modelling objectives

Very complex well geometries

Very low permeabilities

(<0.1mD)

Capture transient effects (non

boundary-dominated flow)

Test fine-scale heterogeneity

(assuming can be modelled)

3D numerical

simulation

Model well types with

analytical methods (skin)

Most traditional CBM plays

Constant drainage per well

(unchanged with time)

Constant average properties

within each well drainage

Multi-well

material balance

Very large area and no wells

>100 wells – field scale

modelling

Medium areas and no wells (5-

20 wells) - Sector modelling

SPEED

Full numerical or material-balance models ? Some of the available tools on market

3D numerical

simulation

Multi-well Material

Balance

ECLIPSE CBM

CONCEPT SELECTION

Key Decisions to be made

• Well Technology : well trajectory and completion

• Spacing : surface and subsurface

• Sectorisation : define areas of incrementally higher unit cost (or other metric)

• Development Intensity : drilling pace and scheduling

Workflows and tools

• Integrating geological models, production forecasting and (proxy) economical screening capabilities

• Focus on key uncertainties’ impact on decisions

Associated Outcomes from the FDP Optimisation

• Decision mapping for future technology trials and appraisal

• High-grading areas of increasing potential

• Defining economic limits and economic sensitivities

Concept Select and Field Development Planning Optimisation Using nested workflows and tools

Technology Trials

Appraisal Strategy

Well technology concept

Spacing

Sectorisation

The Concept Select Workflow Tackling technology, spacing and sector definition in one iteration

An example of Value Metric:

Run alternative technologies

Set up dynamic models Map or Distribution based

Run alternative spacing

Compute Value Metrics

Selection

Selection of Optimum Spacing

for a given technology

Well Drilling and Completion Cost (USD) vs Depth

y = 313.81x + 1E+06

R2 = 0.7954

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

0 200 400 600 800 1000 1200 1400 1600 1800

DEPTH (m)

USD

$

Cost per well model

Technology ‘Concept A’

Depth over the Asset

Production

Forecast

Repeat calculation at

different spacing

Generate field Economic predictions Unit Cost, NPV/well – different spacing

0

1

2

3

4

5

6

7

8

9

10

-600,000,000.0

-400,000,000.0

-200,000,000.0

0.0

200,000,000.0

400,000,000.0

600,000,000.0

0 0.5 1 1.5 2 2.5P

roje

ct N

PV

($

mm

)

Well Spacing (km)

WELL CONCEPT (A) : DEVIATED WELL, MULTI-STAGED FRACSPROJECT UNPV (@ 4.5 $/GJ) AND UDC v WELL SPACING

PROJ NPV UDC

0

1

2

3

4

5

6

7

8

9

10

-600,000,000.0

-400,000,000.0

-200,000,000.0

0.0

200,000,000.0

400,000,000.0

600,000,000.0

0 0.5 1 1.5 2 2.5

Pro

ject

NP

V (

$m

m)

Well Spacing (km)

WELL CONCEPT (A) : DEVIATED WELL, MULTI-STAGED FRACSPROJECT UNPV (@ 4.5 $/GJ) AND UDC v WELL SPACING

PROJ NPV UDC

Optimum

Field NPV

Optimum Unit

Cost ($/GJ)

Selection methodology

Screening an optimum ‘Concept’ depends on the Value-Metric

that is optimised

Workflow is

replicated for

well spacing

and

technology

For an

integrated

selection

Selection

conducted at

sector level

-800,000,000.0

-600,000,000.0

-400,000,000.0

-200,000,000.0

0.0

200,000,000.0

400,000,000.0

600,000,000.0

800,000,000.0

1,000,000,000.0

0.00 0.50 1.00 1.50 2.00 2.50

FIELD XProject Undeveloped NPV v Well Spacing for Technology

Screening

Alternative

technologies

tested

Combined

selection of

spacing and

technology

Selection methodology

Combined spacing and technology selection – an illustration

Dev

Fracc’ed

1.0 km

spacing

Sectorisation of CBM fields Key to economic optimisation

Selection of sector based on

clustering of higher / lower

potential Value-Metric outcomes

SECTORISATION METHODOLOGY

Assign technology

models (skin)

Sector pre-selection-

Alternative scenarios

Non -technical factorsTechnical considerations

Ref Field property mapReserves Categories

Permit Outlines

Facilities Unit Sizes

Run multi - well vs.

multi -technology

cases

Determine

optimum well /

well concept

Field

Level

Analysis

Sector

Level

Analysis

Comparative

Economical

Modelling

Iterate and finalise sector selection

For each well within the considered sector

Run Sector

statistics

Determine sector

optimum well

concept

Identify potential

sector definiton

refinement

Sector 2 Medium Value Metric

Sector 3 Low Value Metric

Sector 1 High Value Metric

Why managing cost is so important for CBM Illustration of the value creation as a combination of increased developable area,

down-spacing and PVed-cost reduction gains

Value delivered vs. Cost structure As Unit Cost reduces, value increases in

steps as new areas become economic

New areas

become

economical

Value creation within

developable area through

down-spacing and NPV

gain through cost

reduction

Sectorisation and areal analysis

is key to optimisation

Value creation

$ NPV

Low Unit Cost High

Gas Price

Conclusions

Conducting CBM field development studies

– Some learnings from Conventional ‘classical’ uses of technology and processes

– But a lot of very specific differences, under a shroud of variability

– Reliant on geostatistics to a large extent

Not all statistics

– Sweet-spotting of net coal thickness locally can positively impact appraisal and reserves build, but also potentially development

– Deliverability and drainage optimisation achieved through integration and mapping of data at different scales

• Lots of uncertainty doesn’t mean a lesser need for rigour

– Do more to assess the consequences of uncertainty at a given time, especially for future concept select refinements

– Trade complex models (3D numerical – where possible) for efficient nested workflows with multi-well material balance