jared philpot principal reservoir engineer arrow energy · pdf filebest practices: coal bed...

BEST PRACTICES:

COAL BED METHANE MODELLING

Jared Philpot

Principal Reservoir Engineer

Arrow Energy

August 2013

ARROW ENERGY DISCLAIMER

ARROW ENERGY SAFETY MOMENT – WHAT COULD GO WRONG?

ARROW ENERGY SUBSURFACE UNCERTAINTY

Typical project dimensions:

• How big is the average CSG-LNG development?

• QGC – 6,000 wells across 4,700 km2*

• GLNG – 2,650 wells across 6,900 km2*

• APLNG – 10,000 wells across 5,700 km2*

• Arrow’s Bowen Gas Project – 6,500 wells across 8,000 km2

• Arrow’s Surat Gas Project - 7,500 wells across 8,600 km2

• Somewhere between the State of Palestine (5,640 km2) and Puerto

Rico (8,870 km2)

* Taken from EIS documents of respective projects

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ARROW ENERGY WHAT DOES SUBSURFACE UNCERTAINTY LOOK LIKE?

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THE CHALLENGE...

• Lots of development

wells drilled early

• Large geographic

area

• Limited data

• Significant uncertainty

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ARROW ENERGY CHALLENGE – A COMPLICATED WORKFLOW

Reservoir

Description

Uncertainty

Range

Dynamic

Model

(10 or so)

Alternative

Reservoir

Descriptions

Well Spacing

Study x (10 or

so)

Well Spacing

RF S-Curve x

10 (or so)

Generate RF-

Depth Curves

Low, Mid, High

Low EUR

Mid EUR

High EUR

Pilot History

Screening

Exploration

Data

Wells, Logs,

Seismic

Experimental

Design

Appraisal Data

Static Model

Well Completions

Chevron, Quad,

MBL

Volumetric

Calculation

Low, Mid, High

Gas & Water Type

Curves

Low, Mid, High

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ARROW ENERGY THE CHALLENGE – IN A NUTSHELL

• Sources of uncertainty

• data density – data distribution across wide area

• data models - accuracy, predictive capability

• pilot production – how to propagate across areas

• Understanding uncertainty

• uncertainty in model predictions given uncertainty in input data

• uncertainty in model itself (how good is the model)

• Living with uncertainty

• capturing uncertainty in development planning

• multiple deterministic cases

• probabilistic framework

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ARROW ENERGY UNDERSTANDING UNCERTAINTY – SIMPLE PERMEABILITY MODEL

Simple permeability model

• K = f (depth)

• model can be defined by

taking a best fit line

through the data points

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ARROW ENERGY

Uncertainty in input data:

• Calculate residuals

between trend and data

measurements

• Analyse residual

distribution

• From CDF choose

P(10), P(90) values and

use these to construct

corresponding trends

UNDERSTANDING UNCERTAINTY – SIMPLE PERMEABILITY MODEL

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ARROW ENERGY UNDERSTANDING UNCERTAINTY – SIMPLE PERMEABILITY MODEL

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ARROW ENERGY

Confidence Intervals :

• Given sample distribution

and size of sample

calculate confidence

interval (95%).

• Function of number of

measurements.

• There is a 95% confidence

the true perm vs depth

trend lies within this

interval.

0.01

0.10

1.00

10.00

100.00

100 200 300 400 500 600 700

Perm

eab

ilit

y (

md

)

Depth (m)

Meas. Low High

CI CI Expon. (Meas.)


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ARROW ENERGY

Impact of Heterogeneity

• Three different methods used to populate Permeability in static

model

• (1) Apply trends directly

• (2) Normal distribution about trend

• (3) SGS about trend

• Impact on EUR assessed using RF = f(Perm, Well Spacing)

b

a

k

WS

RFRF

1

max


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ARROW ENERGY

Mid

Low

High

Impact of Heterogeneity

• Three different methods used to

populate permeability in static model:

1. Apply trends directly

2. Normal distribution about trend

3. SGS about trend


ARROW ENERGY PROPERTY POPULATION: PERMEABILITY

Normal

Distribution

About Trend

SGS Surface

About Trend

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ARROW ENERGY

• The impact of the alternative permeability realizations is relatively

insignificant when compared to the impact of the low, base or high

permeability trend.

• However, this would not be the case if the ultimate recovery was

being estimated for a small sector or for an individual well.

Low Mid High

(1) Apply Trends Directly 0.69 1.00 1.59

(2) Normal Distribution About Trends 0.70-0.71 0.98-0.98 1.60-1.61

(3) SGS Surface About Trends 0.69 0.97 1.58

Normalised EUR


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ARROW ENERGY

• The scatter in the data may be indicative of two different

types of uncertainty:

• the first is related to the heterogeneity of the coal itself

• the second type of uncertainty lies in the position of the trend and

is related to the fact that the current data set is only a limited

sample of the complete population

• Careful attention is required to capture the uncertainty

associated with various correlations developed during

property modeling

• Large range in EUR outcomes.


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ARROW ENERGY

PILOT WELLS

COMPLEX GEOLOGY

CHALLENGING WELL TYPES

UNDERSTANDING UNCERTAINTY – APPRAISAL PILOT PRODUCTION DATA

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ARROW ENERGY UNDERSTANDING UNCERTAINTY – APPRAISAL PILOT PRODUCTION DATA

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ARROW ENERGY

Define Parametric Outcomes

• Reservoir performance outcomes usually assessed include:

• Historical pressure production

• Historical gas and water production

Optimal Case Selection

• Select cases with good pressure match that are within 10% cumulative gas production

and 20% cumulative water production.

• Extract the parameters used to achieve match for Scope of Recovery analysis

Run Multiple realizations using Experimental Design Sampling

• Parameters used in this example are :

• Permeability (anisotropy)

• Porosity

• Relative Permeability

• Coal compressibility

• Desorption time constant (tau)


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ARROW ENERGY

Base case history match

• single combination of input

parameters (permeability, gas

content, etc)

• single model matched to pilot

production.

Dynamic uncertainty workflow

• quickly find alternative

combinations of parameters

• multiple models matched to

pilot production.


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ARROW ENERGY

Case Perm Comp Corey

Gas

Corey

Water

Gas

Content

KvKh KyKx PL Poro Tau VL

Base 300 0.005 3 2 0 0.25 1 29 0.004 90 0

1 Mid Mid Low Mid Low Mid High Mid Low Mid Low

2 Mid Mid Mid High Low Low Low Low Mid Mid Mid

3 Mid Mid Mid Low Low High Low Low Mid Mid Mid

4 Mid Mid Mid Low High Low Low Low Mid Mid Mid

5 Mid Mid Mid High Low High Low High Mid Mid Mid

6 Mid Mid Mid High High High Low Low Mid Mid Mid

7 Mid Low High Mid Mid High Low Mid Mid High Mid

8 Mid Mid Mid Low Low Low Low High Mid Mid Mid

9 Mid Low Low Mid Mid Low Low Mid Mid High Mid

10 Mid High Mid Low High Mid Mid Mid Low High Mid


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ARROW ENERGY LIVING WITH UNCERTAINTY – WELL SPACING OPTIMISATION

Well configurations:

• three well types considered

• sensitivity of production and economics to various spacing, in-seam

lengths considered for each depth.

Well spacing optimisation example

Quad Lateral Chevron Multi-Branch Lateral

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ARROW ENERGY

• a set of reservoir

simulation sector models

are generated to cover

depth range of interest.

• permeability, gas content

from depth trends

(different values in each

model)

• various well

configurations assessed

for each depth.

100 m

200 m

300 m

400 m

500 m

600 m

LIVING WITH UNCERTAINTY – WELL SPACING OPTIMISATION

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ARROW ENERGY

(1) Start with lowest UTC case

(2) Find subsequent case

showing smallest

incremental UTC

Incremental

UTC > Limit?

(3) Found optimal case

No

Yes

(1)

(3)

Incremental UTC =

D ( PV Total Cost )

D ( PV Produced Gas )

For each depth and reservoir:

WELL TYPE A B C

LIVING WITH UNCERTAINTY – WELL SPACING OPTIMISATION

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ARROW ENERGY CAPTURING UNCERTAINTY IN DEVELOPMENT PLANNING

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• Builds GIIP distribution considering:

• Thickness

• Density

• Gas Content

• etc.


Recovery factor curves

• possibility of estimating

probabilistic EUR’s

without running full field

simulation models

• estimate EUR’s for

various well spacing and

well type assumptions.

Development planning

• area wide P10/50/90

profiles for gas and water

for different well types.

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GIIP, EUR Distribution

Load into mapping software

to integrate with other data

and aid decision making Mean GIIP Standard Deviation GIIP

ARROW ENERGY SUMMARY

• Sources of uncertainty

• data density – data distribution across wide area

• data models - accuracy, predictive capability

• pilot production – how to propagate across areas

• Understanding uncertainty

• uncertainty in model predictions given uncertainty in input data

• uncertainty in model itself (how good is the model)

• Living with uncertainty

• capturing uncertainty in development planning

• multiple deterministic cases

• probabilistic framework

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ARROW ENERGY

• Coauthors: Saikat Mazumder, Siddharta Naicker, Gladys

Chang, Mohammad Boostani, Miguel Tovar, Vikram

Sharma

• Arrow Energy for permission to give presentation

ACKNOWLEDGEMENTS

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ARROW ENERGY

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© Arrow Energy Pty Ltd August 2013

While Arrow Energy Pty Ltd has endeavoured to ensure that all information provided in

this publication is accurate and up to date at the time of publication, it takes no

responsibility for any error or omission relating to this information. Furthermore, the

information provided shall not constitute financial product advice pursuant to the

Australian Financial Services Licence held by Arrow Energy Pty Ltd’s related body

corporate. To the maximum extent permitted by law, Arrow Energy Pty Ltd will not be

liable for any cost, loss or damage (whether caused by negligence or otherwise)

suffered by you through your use of this publication.

jared philpot principal reservoir engineer arrow energy · pdf filebest practices: coal bed...

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