Optimizing Production from

Liquid Loading Wells

Shona Neve GWD 2013, Groningen, October 2013

Outline

Introduction

Field Issues

2013 Optimisation Activities

Well Cycling Optimisation

Batch Foam Optimisation

Lessons Learned & Best Practices

2

The Field

A large gas-condensate field located in the UKCS

First gas was around 1998

65% of GIIP recovered to date

Gas is trapped in Lower Cretaceous deepwater sands

Reservoir strongly layered with multiple reservoir intervals separated

by thick shales

Low – Moderate permeability

Developed using long, deviated wells with large tubing IDs

3

Low Rate Well Strategy

4

25% decline rate

18% decline rate

due to Low Rate

Well Strategy

2013 Production Challenge

5

Multiple elements contributing to a

more robust production

performance process:

1. Cycle Well Optimisation

2. Batch Foam Treatment

Optimisation

3. Improving Measurement

Techniques

4. Production Performance

Reviews

0

10

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28-A

ug

-10

30-A

ug

-10

01-S

ep

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03-S

ep

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ep

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ep

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ep

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We

llh

ea

d P

res

su

re [

bar]

0

10

20

30

40

50

60

70

80

Flo

wli

ne

Te

mp

era

ture

[d

eg

C]

Liquid loading well -

frequent low pressure

routing necessary

(cycling)

Liquid loading well post-batch foamer treatment -

less frequent low pressure routing necessary

Well Cycling & Batch Foamer Treatments

6

Well Cycling Optimisation Study

19 liquid loading wells

Maximum of 3 wells through low pressure system at once

Each well takes around 7-8hours to unload & stabilise

7

Cycle Well Uptime Cycle Well Production

Model

8

Time

TH

P [

ba

rg]

TH

T [

de

gC

]

Ga

s R

ate

[m

mscfd

]

THT Lower Limit

Test points obtained by routing well

through Platform Test Separator

Decline rate assumed through test

points

Typical THT decline input &

relationship assumed between THT

& Q

Well Cycling Tool Workflow

9

Input duration

of next cycle

Model

calculates

current Q for

each well

Cum. gas

predicted over

next cycle

Cycle

Advantage

determined

Cycling

Advantages

ranked

PE to QC

results &

perform well

movement

Become

more

predictive

Optimise

Production

vs. Uptime

Batch Foam Treatment Optimisation

Foamer (surfactant) is batch-injected into low rate wells to decrease

the critical gas rate and encourage de-liquification

Foam decreases the density (rL) and surface tension (mL) of the liquid,

in turn increasing the liquid unloading potential

Used on this gas condensate field with original trials 5 years ago

Effectiveness highly variable… why? Optimisation… how?

10

MSc Project Outline – Imperial College

1. Literature Review

External knowledge

Internal data

2. Foam Effectiveness

Well Completion

Geographic Position

Dynamic Effects

3. Foam Optimisation

Injection Volume

Foam Soak Time

4. Field Production Trials

Potential Benefit

11

Ref. 12, 16

Foam Optimisation – Volume?

C

A. Pressure Survey

B. Downhole Gauge

C. Acoustic Survey

Pressure

Dep

th

B

THP

DHG

Downhole Gauge

Liquid Level

Pressure

Dep

th PLT

Liquid Level

A Ref. 4

12

Foam Optimisation – Volume!

E.g.

Wellbore liquid volume +/- < 1.5

barrels

Foam injection volume +/- < 0.05

barrels

QC

13

Well

Details

Foam Optimisation – Time?

Foamer

Viscosity Foamer

Density

Gas

Z-Function Gas

Density

Film Flow Model

Ref. 6, 14, 24, 28

Ref. 8, 13, 19

(Adapted from Jackson 1955)

14

Foam Optimisation – Time!

15

Field Production Trials

Test Case:

1x of ≈ 6-8 regular cycle wells:

Result:

Increased well up-time (+12 hrs/wk)

MP utilisation reduced by ≈ 36 hrs/wk

Extrapolated to one year this is worth

over £2M (at £0.66 / therm)

‘Standard’

‘Optimised’

12HR

16

The Future for the Field

Time

Ga

s R

ate

Low Rate Well Work Over Development

Long Term Compression

2010 New Wells

2011 Capillary Strings

Cycling

Base

17

Best Practices & Lessons Learned

Best Practices

Close offshore & onshore collaboration

is crucial to project success

Never assume a routine activity is fully

optimised

Challenging current processes by

interpreting & gathering the correct data

& streamlining the process can lead to

significant benefits

18

Lessons Learned

Production optimisation can be

successful with minimal associated cost

The combined effect of multiple small

optimisation opportunities can have a big

impact to total production

Questions?

19

References

20

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