Non-intrusive wellhead surveillance to support brownfield production optimisation – recent case studies

Agenda

Introduction

Produce the Technical Limit

Typical Sonar Applications

Sonar Technology

Case Studies

Conclusion

Introduction

Production optimisation of mature fields plays a significant role in todays market

conditions where cost is at the top of the priority list and operators need to find

suitable technologies to support daily operations while keeping costs down without

jeopardizing or shutting down production.

Low oil price brings Optimization into sharp relief

RE

SE

RV

OIR

MA

NA

GE

ME

NT

DA

TA

Wel

ls &

Pip

elin

e D

ata

Pla

nt

Cap

acit

y &

Ch

oke

Mo

del

Dat

a

Pla

nt

Cap

acit

y

Map

pin

g

Pre

ssu

re

Map

pin

g

MANPOWER - RESOURCES

PTL, Cross

Discipline

Facilitation Team

EGIS

Asset, Cross Discipline Team

OPTIMISATION TOOLS

Reservoir Simulators

IAM Toolkit

Process

Simulators

PTL REVIEW

WORKSHOP

System

Bottlenecks

Identified

Opportunities

Identified

Prioritisation of

Opportunities

PTL

Opportunities

are Developed

further,

Resourced,

Costed and

Logged.

Activities

Implemented

Production Gains

Tracked

Produce the Technical Limit

• Wellhead Production Surveillance

• ESP Optimisation

• Gas lift Optimisation

• Pressure Support (water Injection)

• Post and pre intervention

Typical SONAR Applications

SonarTest™

Temporary

SonarMonitor™

Permanent

• Augment well testing regime

• New meter verification during start up

• Legacy metering verification

• Legacy metering replacement

©Copyright Expro 2015

SONAR Technology

ATEX/IECEX Zone 1,

2” to 32”,

Liquid Flow Velocity Range

(application dependant) =

0.5 to 50 m/s (1.5 to 150 f/s)

Gas Flow Velocity Range (application

dependant) =

0.5 to 50 m/s (1.5 to 150 f/s)

Well-suited for dry and wet gas

surveillance in heavy schedule piping.

ATEX Zone 2,

2” to 30”,

Liquid Flow Velocity Range (application

dependant) =

1 to 10 m/s (3 to 30 f/s)

Gas Flow Velocity Range (application

dependant) =

6 to 50 m/s (20 to 150 f/s)

Well-suited for high flow rates, large

diameter pipes and high liquid loadings.

ActiveSONARTM

Meter PassiveSONARTM

Meter

SONAR Technology Specifications

Case Studies

Centrica SNS: Mature Dry Gas (SPE171712)

Marathon NS : Liquid Loading Prone Gas (SPE166652-MS)

Algeria: Mature Oil Field

• Centrica operates South And North

Morecambe Gas Fields

• Production commenced in 1985

• S. Morecambe – 34 active wells, N.

Morecambe – 10 active wells

• Produced via volumetric depletion

• Wells gathered to drilling platforms –

Normally Unmanned Installations

Slide 12

SPE171712 • Application of Sonar Flow Measurement For Field Wide Surveillance Of A Mature Gas Field • Ahmed Hussein

Centrica Case Study: Mature Dry Gas (SPE171712)

• Venturi meters originally installed for both fields (1985 and 1994 respectively)

• Meters sized for peak production, outside measurement range as field

production declined.

• Production separators installed and subsequently removed.

• Rough well allocation performed by analyzing P & T trends.

• Different technologies evaluated before Sonar technology selected

after an initial trial campaign.

Slide 13

Well Allocation and Metering Background

Charlie Facility , Gross 14 MMscf/day,

• 5 wells

• Individual wells ~ 3 MMscf/day

• SONAR Meter diagnostics gave

overwhelming evidence that only 1 out of 5

wells were flowing,

• Flowing well was > 14 MMscf/day

• Production from only 1 well

• Customer made decision to shut in

facility, one well at a time

• Well #4 shut in last, confirmed all

production came from single well

• Wireline intervention downhole camera

highlighted Halite

• Well work program –fresh water Halite

wash and N2 Coil Tubing Lift

• Additional 4 wells were taken back onto

production,

• Overall Field production up.

Initial Meter Validation

• 44 Sonar meters permanently installed

across 6 platforms

• Meters directly clamped onto pipe, no flow

interference or pressure loss

• Short rig-up time

• Significantly lower production losses as

compared to individual well testing using

test separators.

• Availability of real time flow information for

each well.

Slide 15

SONAR Well Surveillance

Slide 16

0

1

2

3

4

5

6

7

Ap

r-1

2

Ma

y-1

2

Jun

-12

Jul-

12

Au

g-1

2

Sep

-12

Oct

-12

No

v-1

2

De

c-1

2

Jan

-13

Feb

-13

Ma

r-1

3

Ap

r-1

3

Ma

y-1

3

Jun

-13

Jul-

13

Au

g-1

3

Sep

-13

Oct

-13

No

v-1

3

De

c-1

3

Jan

-14

Feb

-14

Ma

r-1

4

Ap

r-1

4

Ma

y-1

4

Jun

-14

SO

NA

R Q

ga

s (M

msc

fd)

Well Alpha1 - Alpha Platform

46.3

24.2

6.2

47.2

54.2

0

10

20

30

40

50

60

70

SON

AR

Qga

s (M

Msc

fd)

Alpha Beta Charlie Delta Gamma

All Platforms (S. Morecambe) - 08 June 2014

1.9 1.90.2

6.1

0.7

10.2

0.6

25.1

7.5

0

5

10

15

20

25

30

SON

AR

Qga

s (M

Msc

fd)

X1 X2 X3 X4 X5 X6 X7 X8 X9

Gamma Platform - 08 June 2014

SONAR Real Time Data

• Following the start up of a new, high pressure field, wells on DPPA

platform dropped off in performance.

• Sonar meters used to establish worst affected wells and analyze

response to cycling procedures.

• Determination of the optimum shut in period is a trail and error

exercise until a suitable ratio is established

Slide 17

Well Production Optimization – Well Cycling

• Cycling program was then extended to other wells in the fields

• Wells responding to a cycling routine were subjected to batch foam treatment to

help unload larger amounts of liquid

• Selected wells from the batch program put forward as candidates for permanent

foam injection.

• Sonar test data used to diagnose unstable wells (due to liquid loading and/or other

issues)

• After Sonar data evaluation, PLT (Production Logging Test) runs are scheduled

for wells displaying unstable behavior due to liquid loading

Slide 18

Well Production Optimization – Well Cycling

90

86

75

6762

40

57

50

35

15 Test/ LP Sep

30

37

Se

pa

rato

r P

ressu

re (

Ba

rs)

Gas recycling

to maximise

condensate

recovery Gas Exporting

Blow-down

stage

Reducing

Separator

Pressure

with

Production Decline.

28 HP Sep

Marathon

• East Brae mature Gas Condensate Field

• Field in Production blow down

• Water Influx from active reservoir

• Mixture of core and lazy wells

• Well allocation using individual well

testing

Marathon Case Study: Liquid Loading Prone Gas (SPE166652-MS)

$

Production

Wells Injection

Wells

Power,

Fluid, Gas

Cap Rock Gas

Oil

Water

RESERVOIR

Facilities

A complete systems

approach

Water

Case Study: Liquid Loading Prone Gas (SPE166652-MS)

• Flow assurance

• Shell deliquifaction modelling tool

• Marathon Oil’s gas modelling

• Vertical lift performance

• In-flow performance

• Critical rate at HP & LP

Separator Pressures

• Production constraints

• Process modelling

Actions

• Convert test separator to LP

Production

• Maximise production at or below

critical gas rate

• Intensive well manipulation with

focused surveillance

• Pro longed Sonar well tests

• Temperature monitoring

• Production logging

interventions

Data Comparison

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

Jun-10 Oct-10 Feb-11 Jun-11 Oct-11 Feb-12 Jun-12 Oct-12

Qg

as (

MM

scfd

)

A3 Sonar Test Data

A3 Separator Test Data

19.1

8.4

12.8

18.6

27.9

18.3

7.5

11.4

17.9

25.3

0.0

5.0

10.0

15.0

20.0

25.0

30.0

A01 A02 A04 A05 A06

Qg

as (

MM

scfd

)

Wells

Test Separator Rates Sonar test rates

• Bi-monthly Surveillance clamp-on well

testing

• Clamp-on hardware is permanently

mounted to wellhead piping for

consistency of measurement and well

site efficiency

• Comparison of data from the test

separator gas meter and the sonar

meters was carried out.

• The percentage difference in

measured Qgas (volumetric gas flow

rate at standard conditions) varies

between 0% and 10%.

• Work with regulatory authorities to

have SONAR Meter accepted as

alternative to conventional Test

Separator based well tests

well A3 over a period of 2 years.

East Brae.

©Copyright Expro 2013 - STM00003 Rev 01

28.0

28.2

28.4

28.6

28.8

29.0

29.2

29.4

29.6

29.8

30.0

0.0

5.0

10.0

15.0

20.0

25.0

Pro

ce

ss

Pre

ss

. (b

arg

)

Qg

as

(M

Ms

cfd

)

Time

Qgas @ Std Conditions Process Pressure

0

5

10

15

20

25

30

35

40

.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

Pro

cess P

ress.

(barg

)

Qg

as (

MM

scfd

)

Time

Qgas @ Std Condiitions Process Pressure

Well closed in to change

compressors

HP

separator

HP

separator

LP

separator

• Use SONAR Meters to test core wells

when Test Separator is not available.

• Use of SONAR Meters to investigate

individual well performance in LP

Separator

• Identify performance based criteria for

well swinging operations

• 12 wells on 4hr swing cycle

• LP separator can accommodate

Maximum 3-5 wells

• Lazy wells on Huff and Puff

• Achieve 90% utilization of LP Separator

for well unloading,

Huff & Puff plus HP to LP Swing

Arrest Field Decline.

Ga

s P

rod

uct

ion

Decline pre testseparator crossover

Decline post test separator crossover

Arrested Decline

Algeria Water / Gas injection Case Study

Hassi Messaoud Mature Oil field 1956.

Super Giant reservoir with total proven

reserves of 6.4+ billion barrels of oil.

• Large scale water and gas injection

network

• Need for understanding and optimising

injection of increased importance in

current environment

• Identifying and trouble shooting gaps in

existing data

• Gas lift Optimisation

• Reservoir Pressure Support

• Gas Injection rates

• Water injection rates

• Multirate tests

Gas Lift Measurement -Algeria

• ActiveSONAR used to measure gas lift rates during production testing

• Expro provides personnel and equipment data analysis and reporting

• One test per day

• Measure lift gas flow rate at wellheads and manifolds

• Enhance the value of the ongoing production testing TMU package offering

Gas Lift Optimisation -Algeria

3565

2369

1777

3821

3088

2350 3.11 3.34 3.41

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0

500

1000

1500

2000

2500

3000

3500

4000

4500

14/03/2015 15/03/2015 16/03/2015

SONAR Gas Injection (Sm3/h)

Produced Qgas Av (Sm3/h)

Produced Qoil Av (Sm3/h)

GAS Lift optimization

• Hydrocarbon lifting optimisation is enabled by simultaneous measurement of lift gas and production rates.

• Couple Sonar with PassiveSONAR for production measurement

• Single phase injection and gas lift is on

going

• SONAR now being deployed to monitor

production separator outlets to identify and

monitor cycling behaviours

• Next step is audits in the Production Plant

where client is experiencing instabilities

and uncertainties in the volumetric

behaviour

• Client solution Multiple SONAR Packages

run simultaneously

• measure the production rates from

the wells, manifolds, separators and

export lines

Algeria Production Plant

Sonar Surveillance aids production Optimisation

Sonar Surveillance has had major impact in aiding production allocation from

individual wells while minimizing separator well testing frequency and associated

production loses

Sonar Surveillance, in conjunction with other methods such as temperature

monitoring and PLTs, has helped to understand individual well performance and

analyze the impact of liquid loading

Sonar Surveillance have enabled the production engineering team to identify

underperforming wells and implement short-term de-liquification strategies such

as well cycling

Sonar Surveillance is able to evaluate the effectiveness of interventions and select

suitable candidates for further intervention work, helping to prolong the field life of

the mature asset

Once adopted the scope of work and value information invariably increases

Slide 29

Conclusions

The use of non conventional technologies such SONAR allows for;

Continuous Well and Reservoir Monitoring without process interruptions

Understanding individual well behavior

Real Time Data Collection

Optimize and evaluate well intervention activities

Access to Remote locations

Minimize HSE risks

Increase frequency of testing

Cost reduction

More Quality Data = Better field management = Increased Production

Conclusions

Questions

Any Questions?