Eltazy KhalidBona Prakasa, Morteza Haghighat, Khafiz Muradov, David Davies

Joint Industry Project “VALUE FROM ADVANCED WELLS” (JIP VAWE)Institute of Petroleum Engineering, Heriot-Watt University,

Edinburgh, UK

Production enhancement and uncertainty reduction by optimum

use of flow control devices

2

Presentation outline

Introduction to the advanced completion technology

Active ICV (Interval Control Valves)

Passive ICD (Inflow Control Devices)

Autonomous FCD (Flow Control Devices)

3 IntroductionReactive control of multi-zone production

Reactive • Decisions are based on

system’s current condition

• Short-term objectives: Improve current Production

• React quickly to unexpected events with:

1. Well intervention or

2. ICVs/Autonomous FCD: which introduce extra complexity, risks and limits to the number of zones

4 IntroductionProactive control of multi-zone production

Proactive• Starts during early production

period to mitigate future problems.

• Long-term objective: Increase Total Oil Recovery

• Uses a reservoir model of unknown quality

• Computationally demanding

• Requires reservoir simulation and production modelling skills

• ICVs, ICDs, AFCDs

5

Many types

Tube

EQUIFLOW

Orifice

FloReg & Fluxrite

Slot

Hybrid EQULAIZER

Nozzle

ResFlow, ResInject

ICDHelical

Production EQULAIZER

Passive Control

Inflow control devices (ICD)

Technology developmentAdvanced Well Completion (Downhole Flow

Control) types

Active ControlInflow/Interval Control

Valves (ICV)

New developments

Restrictunwanted fluid flows

AICD

Stop unwanted fluid flows

AICV

LaminarΔP ~ μQ

TurbulentΔP ~ ρQ2

on/off

discrete positions

infinitely variable

hydraulic control

Electriccontrol

Electro-hydraulic

Labyrinth

ICD

6 Active Control (ICVs)Providing a flexible real time control of zonal production

Challenges in proactive optimisation of ICVs:1. Large number of control variables

Number of controlled elements times production time. Example (A real-field case study): 4 years control period, 4

control steps per year (control every 3 months), 12 ICVs 192 variables

Fast and efficient in-house optimisation algorithm developed (SPE-167453).

2. Uncertain numerical reservoir models calculate oil/water production forecast (objective function)

7

This control scenario provides optimal performance for the base-

case model (or realisation).

HOWEVER, this control strategy is highly unlikely to provide optimal performance when applied to (all) other reservoir model realisations

The optimum control scenario is calculated using a single realisation (e.g. base-case).

Problem Definition:Impact of Reservoir Model Uncertainty on Proactive Optimisation using single realisation

Proactiveoptimisation

Water Injector

Gas Injector

Intelligent Producer

Base-case

Optimum control scenario

8

A modified objective function is defined as mean of a reasonable ensemble of realisations. Search for a control scenario which improve all realisations (to

some extent)

Solution:Developed Approaches for Proactive Optimisation under Uncertainties (Robust Optimisation)

0.0E+0

2.0E-4

4.0E-4

6.0E-4

8.0E-4

1.0E-3

570 670 770 870

Prob

abili

ty D

istr

ibut

ion

Func

tion

NPV (MM$)

Fully-open ICVs (Base-case)

Single realisation optimisation

Robust mean optimisation

Details available in: “Reservoir uncertainty tolerant, proactive control of intelligent wells”. Haghighat Sefat, M., et al. Computational Geosciences. 2015.

Optimum control scenario obtained using Robust Mean Optimisation is applied to all realisations: Increased mean

(maximum added-value) Reduced uncertainty

Optimum control scenario obtained using Single Realisation Optimisation is applied to all realisations: Non-optimum

performance in some realisations

9

Many types

Tube

EQUIFLOW

Orifice

FloReg & Fluxrite

Slot

Hybrid EQULAIZER

Nozzle

ResFlow, ResInject

Labyrinth

ICD

Helical

Production EQULAIZER

Passive Control

Inflow control devices (ICD)

Technology developmentAdvanced Well Completion (Downhole Flow

Control) types

Active ControlInflow/Interval Control

Valves (ICV)

on/off

discrete positions

infinitely variable

hydraulic control

Electriccontrol

Electro-hydraulic

New developments

Restrictunwanted fluid flows

AICD

Stop unwanted fluid flows

AICV

LaminarΔP ~ μQ

TurbulentΔP ~ ρQ2

10

- Horizontal wells have extended reservoir-well contact

- This results in uneven inflow profile in Open-Hole (OH) wells

- Early water breakthrough- Decreases oil recovery- Well out-flow & surface

separation problems

∆P Reservoir

∆P ICD

• ICD completion reduces the open hole’s inflow rate variation

• The pressure drop across an ICD is position & flow rate dependant

• Analytical & well simulators provide a snapshot of the well inflow performance

• Reservoir Simulators quantify the value equalising the well’s inflow performance

Courtesy of Halliburton

Advanced Well Completions in Heterogeneous Reservoirs

11

0

0.5

1

1.5

2

2.5

3

3.5

4

0

0.5

1

1.5

2

2.5

3

3.5

4

0 200 400 600 800 1000

U-U

CD

(Spe

cific

Inflo

w o

f IC

D C

ompl

etio

n)

Sm

3/d/

m

U-O

H (S

peci

fic In

flow

of O

H C

ompl

etio

n)

Sm

3/d/

m

Measured Depth, m

U-oh (Specific Inflow of OH Completion) U-icd (Specific Inflow of ICD Completion)

• The analytical model of an ICD completion in heterogeneous reservoirs is described• The inflow distribution is quantified and designed

Birchenko et al. 2012, dx.doi.org/10.1016/j.petrol. 0121.06.022)

Optimising & Quantifying the Value of an ICD completion Analytical modelling of the performance of a specific well

<U>OH

<U>ICD

PR = <U>ICD<U>OH

IV = level of reservoir heterogeneity 𝑈𝑈𝑚𝑚−𝑈𝑈1𝑈𝑈𝑚𝑚

IE = IVICDIVOH

12

Productivity Ratio (PR)

How much well productivity is sacrificed

Inflow variance (IV)

Level of reservoir heterogeneity

Inflow Equalisation (IE) = IVICDIVOH

How successful is the ICD-completion

Definition of the Terms used in Selection of ICD-completion to compromise the improved inflow rate profile and the increased

completion pressure losses

Dimensionless Parameters:SPE 175448-MS

13

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Inflow Equalisation 0.9 Inflow Equalisation 0.8 Inflow Equalisation 0.7 Inlfow Equalisation 0.6 Inflow Equalisation 0.5

Inflow Equalisation 0.4 Inflow Equalisation 0.3 Inflow Equalisation 0.2 Inflow Equalisation 0.1

Prod

uctiv

ity R

atio

ICD-completion performance Type Curves for Heterogeneous Reservoirs

SPE 175448

Inflow Variation

More heterogeneous reservoirs require larger reductions in Productivity Ratio for given level of IE

Example :• Reservoir heterogeneity ≡ 0.6 IVOH• Recovery increases with IE = 0.3• Achieved with new PR = 0.20

• Each IE value has a unique type curve that relates IV to PR• ICD completion strength determines PR & IE

14

Original Model

Updated Model

The updated model is more heterogeneous than original

Case studyAdjusted Completion Design When the Well Log shows More Heterogeneous K

distribution than was initially assumed in the well completion model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Prod

uctiv

ity R

atio

Inflow VariationOH(IVOH)

Inflow Equalisation 0.9 Inflow Equalisation 0.8 Inflow Equalisation 0.7 Inlfow Equalisation 0.6 Inflow Equalisation 0.5Inflow Equalisation 0.4 Inflow Equalisation 0.3 Inflow Equalisation 0.2 Inflow Equalisation 0.1

Original design (using initial model) Keep the original ICD size in the updated modelAdjusted ICD size in the updated model

SPE 175448-MS

15 Technology developmentAdvanced Well Completion (Downhole Flow

Control) types

New developments

Restrictunwanted fluid flows

AICD

Stop unwanted fluid flows

AICV

LaminarΔP ~ μQ

TurbulentΔP ~ ρQ2

Many types

Tube

EQUIFLOW

Orifice

FloReg & Fluxrite

Slot

Hybrid EQULAIZER

Nozzle

ResFlow, ResInject

ICDHelical

Production EQULAIZER

Passive Control

Inflow control devices (ICD)

Labyrinth

ICD

Active ControlInflow/Interval Control

Valves (ICV)

on/off

discrete positions

infinitely variable

hydraulic control

Electriccontrol

Electro-hydraulic

16Autonomous Flow Control Devices (AFCDs)

Commercial or with reported engineering development

SPE 166285SPE 159634 SPE 169233-MS

17

05

1015202530

0 20 40 60Pres

sure

dro

p (b

ar)

Flow rate (m3/d)

05

1015202530

0 20 40 60Pres

sure

dro

p (b

ar)

Flow rate (m3/d)

05

1015202530

0 20 40 60

Pres

sure

dro

p (b

ar)

Flow rate (m3/d)

0%

10%

20%

30%

40%

50%

% =

(wat

er, g

as, s

team

)

The target is to understand Optimum completion configuration

Detailed discussion in [SPE 170780-MS]

18

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1.5

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1

1.5

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2.5

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0 10 20 30 40 50 600

0.5

1

1.5

2

2.5

3

0 10 20 30 40 50 600

0.5

1

1.5

2

2.5

3

63

63

63

63

63

Model 1 Model 2

Model 3Model 4

Model 5(x) (x)

(x)(x)

(x)

(y)

(y)

(y)

(y)

(y)

Maximum recovery within white boxes

• Colour = oil recovery (red is more)

• (Y) = restriction to 100% water/gas (equiv. diam. mm)

• (X) = restriction to 100% oil (equiv. area mm2)

Is the optimum AFCD performance similar for different reservoir types and oil/water/gas properties?

• AFCD performance trend is similar• Optimum AFCD performance areas due to:

• Reactive vs. Proactive Control• “Good Water”

Homogeneous reservoir Heterogeneous reservoir

Well crossing a faultSuper K permeability

Analogues to a real case

(IPTC 17977)

19

1.522.533.54 1234

2.5

3

3.5

4x 106

1.522.533.54 1234

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8x 106

Cu

mu

lati

ve O

il (S

m3 )

A B

AFCD with 1 mm equivalent shut-in diameter

Cu

mu

lati

ve O

il (S

m3 )

A B

AFCD with 3 mm equivalent shut-in diameter

Analogous ICD-completion AFCD completion

Field Study:AFCD-completion vs. ICD-completion

Impact of reservoir uncertainty

4 joints per wellbore segment. 3 AFCDs per joint.

0

10

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30

40

50

0 10 20 30

Pres

sure

dro

p (b

ar)

Flow Rate (Sm3/d)

Equivalent Nozzle (Δp α Q2)

Optimum design for an existing AFCD

Oil - Equivalent Nozzle water - Equivalent Nozzle

• Confirmed AFCD Performance Trend• Evaluated role of Uncertainty

AFCD

ICD

Prob

abili

ty D

istr

ibut

ion

Func

tion

Cumulative oil production (SM3)0 10 20 30 40 50 600

0.5

1

1.5

2

2.5

3

63

Model 5

(x)

(y)

0 1 2AFCD Shut in Diameter (mm)

FOPT

8.6% more recovery

• Heavy oil/water• 3 Multi-Lateral wells

20

Improve production control

Improve production in uncertain conditions

Improve project economics

Downhole Flow Control Completion:Added value

21 Acknowledgements

The authors wish to thank :1. The 2nd Inwell Flow Surveillance and Control Seminar

organising committee and the session chairmen for theopportunity to give this presentation.

2. The research leading to these results received partialfunding from the European Union’s Seventh FrameworkProgramme managed by REA-Research ExecutiveAgency http://ec.europa.eu/rea (FP7/2007-2013) undergrant agreement No. FP7-SME-2013-2-605701.

3. Funding was also provided by the sponsors of the “Valueof Advanced Wells” Joint Industry Project at Heriot-WattUniversity.

4. Schlumberger Information Systems are alsoacknowledged for providing access to their software

Thanks For Your Attention.