optimising sweep in the north sea - owi apac 2018:...

Optimising Sweep in the North Sea November 5th 2014,

Dr. Branimir Cvetkovic

Marsk Oil and Gas AS, Copenhagen-Denmark

* Bayerngas Norge AS, Oslo-Norway

• Motivation

• Objectives

• Main Challenges

• Well Water Injection and Production

• Analytical Screening Processes

• Risk Analysis Workflow

• Reservoir to Network Simulation

• Concluding Remarks

• Acknowledgments

Optimizing Sweep in The North Sea | page 2 O

VE

RV

IEW

CHALLENGES

• North Sea mature fields are declining

• Water injection combined with WAG and SWAG appear to be most dominant recovery processes

• Infill drilling activities together with special water treatment improves recovery

• Other EOR pilot studies are challenging for subsea assets

• Alkaline-Surfactant Polymer flooding remains active

• Downhole Water Separation not Faesible

ACTIVITIES

• Understand the importance of having a good reservoir model with high resolution in order to avoid hazards and analyse any changes within the reservoir

• Assess the technology gaps and breakthroughs that are essential in improving the way that IOR is carried out to ensure improvements in your future projects

• Review the ways in which you can tackle the decline in production from your oil and gas wells through a solid optimisation study

Optimizing Sweep in The North Sea | page 3 M

OT

IVA

TIO

NS

D

EC

ISIO

NS

AN

D T

IMIN

G

Optimizing Sweep in The North Sea |

Surface Model

Wellbore Model

Well Model

}Reservoir Model

Horizontal

Well

Vertical Well

CO

MP

LE

XIT

Y a

nd

DE

LIV

ER

AB

ILIT

Y

Finite Difference versus Diffusion Model

page 6

• Con

• Mono-phase

• Simple model

• Limited heterogeneity

• Pro

• No SCAL data

• No upscaling

• No well property distribution

• Reservoir-Fracture-Well simple input

• Individual fracture rates

• Fast

After: AAPG Annual Convention and Exhibition 2013

AN

ALY

TIC

AL

DIF

FU

SIO

N F

LO

W


Field A - North Sea

• Horizontal well with fractures

• 9 injection wells

• 11 production wells

• Provided data for:

• 2-oil production wells

• 14 fractures

• A water injection well

• 16 fractures

Model V

alid

ation

Nu

meri

cal

vs. A

naly

tic

al – S

cre

en

ing

Meth

od

s


Field V - North Sea

0

1

2

3

4

5

6

7

0 100 200 300 400 500 600 700

Time t (Days)

Pro

du

cti

vit

y In

de

x

PI (b

bl/(D

ay

Ps

i)

PI, Productivity index



Fracture Permeability, kf (Fracture width w=0.008 ft)

50 D

15 D

2.5 D

WELL DATA

MFHOW-MODEL DATA

Model V

alid

ation

Pro

ductivity I

ndex P

I [b

bl/(d

Psi)]

Time t (d)

0

1

2

3

4

5

6

7

0 100 200 300 400 500 600 700

Time t (Days)

Pro

du

cti

vit

y In

de

x

PI (b

bl/(D

ay

Ps

i)




Fracture Permeability, kf (Fracture width w=0.008 ft)

50 D

15 D

2.5 D

WELL DATA

MFHOW-MODEL DATA

An

aly

tic

al – P

rod

ucti

on

Scre

en

ing

Meth

od

s


The Model vs.

Observed Cumulative Production Match

Lf PP

36 50

34 40

20 40 Cum

ula

tive p

roduction Q

(bbl)

Time t (d)

Model V

alid

ation

An

aly

tic

al – P

rod

ucti

on

Scre

en

ing

Meth

od

s


IBC Changing from

Constant Rate to Constant Pressure SLAB MODEL - RESTART OPTION

Constant Rate to Constant Pressure IBC Changes

0

500

1000

1500

2000

2500

3000

3500

4000

0 100 200 300 400 500 600 700

t (Days)

Pre

ssu

re P

(P

si)

0

1000

2000

3000

4000

5000

6000

7000

8000

Rate

q (

bbl/D

ay)

IBC of Constant:

Rate

Pressure

Variable

Wellbore Rate

Wellbore Pressure

Imple

menta

tion

Time t (d)

Rate

q (

bbl/d)

Pre

ssure

diffe

rence Δ

p (

psi)

An

aly

tic

al – P

rod

ucti

on

Scre

en

ing

Meth

od

s


Maching Production History

0.1

1

10

100

1000

10000

100000

0 500 1000 1500 2000 2500 3000 3500

Time, t (Days)

Pre

ss

ure

Dif

ffe

ren

ce

, P

i-P

wf

(ps

i)

0

1

10

100

1000

10000

100000

Model - Variable Pressure Difference, Pi-Pwf Constant Rate, q IBC Well_Pressure_Difference

Conatant Pressure, Pi-Pwf IBC Model - Variable Well with Fractures Rate, q Well_Rates

Back to menu Print Save

Constant Rate, IBC

Constant Pressure, IBC Pressure-difference

varies

for the constant Rate varies for the

constant Pressure-difference IBC

for the constant rate IBC

Time t (d)

Pre

ssure

diffe

rence [

Pi-

Pw

f] (

psi)

Rate

q (

bbl)/d

0.1

1

10

100

1000

10000

100000

0 500 1000 1500 2000 2500 3000 3500

Time, t (Days)

Pre

ss

ure

Dif

ffe

ren

ce

, P

i-P

wf

(ps

i)

0

1

10

100

1000

10000

100000

Model - Variable Pressure Difference, Pi-Pwf Constant Rate, q IBC Well_Pressure_Difference

Conatant Pressure, Pi-Pwf IBC Model - Variable Well with Fractures Rate, q Well_Rates

Back to menu Print Save

Constant Rate, IBC

Constant Pressure, IBC Pressure-difference

varies

for the constant Rate varies for the

constant Pressure-difference IBC

for the constant rate IBC

Model V

alid

ation

An

aly

tic

al – P

rod

ucti

on

Scre

en

ing

Meth

od

s

IBC Changing from Constant Rate to Constant Pressure


Stepwise Constant Rate IBC

(1 and 3 intervals)

0

500

1000

1500

2000

2500

3000

3500

4000

0 500 1000 1500 2000 2500 3000 3500

Time, t (Days)

Pre

ssu

re D

iffe

ren

ce, P

i-P

wf

(psi

)

0

2000

4000

6000

8000

10000

12000

Wel

lbo

re r

ates

qin

_HO

W (

bb

l/Day

)

P, Pressure difference Press_Diff_Well_Data qin, Input flow rate

Match

Imple

menta

tion

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 500 1000 1500 2000 2500 3000 3500

Time, t (Days)

Pre

ssu

re D

iffe

ren

ce,

Pi-

Pw

f (P

si)

Pressure Difference (Model) Pressure Difference (Well Data)

Match

Time t (d)

Pre

ssure

diffe

rence Δ

p (psi)

Rate

q (

bbl/d)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 500 1000 1500 2000 2500 3000 3500

Time, t (Days)

Pre

ssu

re D

iffe

ren

ce,

Pi-

Pw

f (P

si)

Pressure Difference (Model) Pressure Difference (Well Data)

Match

Time t (d)

Pre

ssure

diffe

rence Δ

p (psi)

An

aly

tic

al – P

rod

ucti

on

Scre

en

ing

Meth

od

s


Stepwise Constant

Pressure IBC (3 intervals)

Cumulative production Q (bbl)

Ind

ivid

ua

l F

ractu

re R

ate

q (

bb

l/d

)

Ra

te q

(b

bl/d

)

Infinite Conductivity Fracture

Finite Conductivity Fracture

An

aly

tic

al – P

rod

ucti

on

Scre

en

ing

Meth

od

s


Matching Water Injection

0

5000

10000

15000

20000

25000

30000

0 500 1000 1500 2000 2500

Time, t (Days)

Wa

ter

inje

cti

on

ra

te,

q (

bb

l/D

ay

)

0

5000000

10000000

15000000

20000000

25000000

30000000

35000000

40000000

Cu

mu

lati

ve

wa

ter

inje

cti

on

, Q

(b

bl)

Model - Water injection rate, qwWater Injection Rate Model - Cumulative water injection, QwFracture injection rate (equal for fracture 1 and 14)Fracture injection rate (equal for fracture 7 and 8)Well cumulative water injection

Time t (d)

Inje

ction r

ate

q (

bbl/d)

Qum

ula

tive inje

ction Q

(bbl)

0

5000

10000

15000

20000

25000

30000

0 500 1000 1500 2000 2500

Time, t (Days)

Wa

ter

inje

cti

on

ra

te,

q (

bb

l/D

ay

)

0

5000000

10000000

15000000

20000000

25000000

30000000

35000000

40000000

Cu

mu

lati

ve

wa

ter

inje

cti

on

, Q

(b

bl)

Model - Water injection rate, qwWater Injection Rate Model - Cumulative water injection, QwFracture injection rate (equal for fracture 1 and 14)Fracture injection rate (equal for fracture 7 and 8)Well cumulative water injection

Model V

alid

ation

An

aly

tic

al – W

ate

r In

jecti

on

Scre

en

ing

Meth

od

s


Oil Recovery Analytical Injection Rates & Buckley-Leverett

Title of presentation | page 15 A

naly

tic

al – W

ate

r In

jecti

on

Scre

en

ing

Meth

od

s

Reservoir, Well and Fracture Input (Horizontal Well with N Fractures)

• Reservoir

• Isotropic

• Non-isotropic

• Horizontal well

• No flow to the wellbore

• Direct flow to the wellbore

• Wellbore friction

• Model Boundary Conditions

• Inner BC

• Outer BC

Imple

menta

tion

An

aly

tic

al – S

cre

en

ing

Meth

od

s


Effective radius and effective half-length

Horizontal

Well

Fracture

Fracture

Vertical Well

Conclu

din

g R

em

ark

s

An

aly

tic

al – W

ell P

rod

ucti

on

Eff

ecti

ve E

sti

mate

s

Late-Time Approximations for Rates

Rate vs. time match for:

• A fractured horizontal well (2 transversal fractures) vs. vertical well with calculated effective radius

• A fractured horizontal well (3- transversal fractures) vs. a single transversal fractured horizontal well with calculated effective half-length

• A fractured horizontal well (3- longitudinal fractures) vs. a single longitudinal fractured horizontal well with calculated effective half-length

0

1000

2000

3000

4000

5000

6000

7000

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Time, t (d)

Rate

, q

(b

bl/d

)

Model data for a horizontal well with two fractures Calculated data for a vertical well with an equivalent well radius

A transient rate calculated data

for the vertical well with a

derived effective well radius

A horizontal well with two

transversal fractures

0

1000

2000

3000

4000

5000

6000

7000

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Time, t (d)

Rate

, q

(b

bl/d

)

Model data for a horizontal well with two fractures Calculated data for a vertical well with an equivalent well radius

A transient rate calculated data

for the vertical well with a

derived effective well radius

A horizontal well with two

transversal fractures

0

2000

4000

6000

8000

10000

12000

14000

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Time, t (d)

Rate

, q

(b

bl/d

)

Well with the 3 transversal-fractures Well with the single transversal-fracture (with the equivalent fracture half-length)

A transient rate calculated data for the

horizontal well with a derived

equivalent fracture half-length

A horizontal well with three transversal fractures

0

2000

4000

6000

8000

10000

12000

14000

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Time, t (d)

Rate

, q

(b

bl/d

)

Well with the 3 transversal-fractures Well with the single transversal-fracture (with the equivalent fracture half-length)




A horizontal well with three transversal fractures

0

2000

4000

6000

8000

10000

12000

14000

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Time, t (d)

Rate

q (

bb

l/d

)

Well with the 3 longitudinal-fractures Well with the single longitudinal-fracture (with the equivalent fracture half-length)




A horizontal well with three longitudinal fractures




A horizontal well with three longitudinal fractures

Model V

alid

ation

ref

Lef

An

aly

tic

al – W

ell P

rod

ucti

on

Eff

ecti

ve E

sti

mate

s

Exponential

Hyperbolic

Harmonic

q

t

b

Rate-Time Plots M

otivation

• Empirical Arps’ rate-time curves (1945)

• Fetkovich’s composite transient-depletion rate-time curves (1973, 1980)

rD

T

D Mu

lti-

fractu

red

well t

ran

sie

nt

so

luti

on

s


Fracture Diagnosis Analyses

• Study is based on the screening analysis of a horizontal well with fractures production data.

• Prognosis profiles were generated manually and compared to the real observed data.

• Study provides workflow for optimising number of fractures along a horizontal well.

• Matching procedure should be further improved with risking tool features for the fracture closure diagnosis.

• The semi-analytical model for a multiple-fractured-horizontal well, was applied providing fast and robust features and were used as screening tools for diagnostic and forecasting purposes.

Conclu

din

g R

em

ark

s

Mu

lti-

fractu

red

well s

olu

tio

ns


Phase C: History Match of Observed Well Data with the Model Obtained Data

PREDICTION SCENARIO

UNCERTAINTY

PARAMETERS

RESPONSE

PARAMETERS

SENSITIVITY ANALYSIS

PROBABILISTIC FOREACASTING

UNCERTAINTY QUANTIFICATION

PROJECT OPTIMIZATION

hig

h

me

an

low

Fracture

Half-length

Height

Spacing

Conductivity

hf

Lf

ΔL

Finite Conductivity

•Kf fracture

permeability

•W width

Ris

k A

naly

sis

- W

ork

flow

M

atc

hin

g H

isto

ry w

ith

Ris

k A

naly

sis


Define the Model

Gathering Data

Semi-Analytical Simulations

Of the Provided Input

Using the Model Reusults Match Observed Well with Fractures Data

0

5000

10000

15000

20000

25000

30000

0 2000000 4000000 6000000 8000000 10000000 12000000 14000000 16000000 18000000

Cumulative rate, Q (bbl)

Rate

, q

(b

bl/

Day

)

Model flow rate, q Well rate, qRis

k A

naly

sis

- W

ork

flow

Phase A: Matching production profiles for the Fixed Number of Fractures

Matc

hin

g H

isto

ry w

ith

Ris

k A

naly

sis

Phase B: Screning Analysis and Optimizing Number of Transversal Fractures Positioned Along a Horizontal Well

PREDICTION SCENARIO

UNCERTAINTY PARAMETERS

RESPONSE PARAMETERS

SENSITIVITY ANALYSIS

PROBABILISTIC FOREACASTING

UNCERTAINTY QUANTIFICATION

PROJECT OPTIMIZATION

•Define ‘Base

Case Model’-

pre-screening

process

hig

h

me

an

low

Reservoir data

Fracture data

Well data

Ris

k A

naly

sis

- W

ork

flow

M

atc

hin

g H

isto

ry w

ith

Ris

k A

naly

sis

2

FRACTURES

hig

h

me

an

low

Semi-analytical Input

Reservoir, Fracture, Well

Data

Semi-analytical output

FRACTURE POSITIONING OPTIMIZATION

Semi-Analytical Fractured

Horizontal Well Simulator

Network Simulator

Risk Analyses

Tool

Ris

k A

naly

sis

with N

etw

ork

-Reserv

oir

Sim

ula

tion

Reserv

oir

to

Su

rface F

low

Mo

dellin

g

Integral Approach Horozontal Well with Fractures

MATCHING

PROCEDURE

SEMI-

ANALYTICAL

MODEL

- HOWIF

NETWORK

SIMULATOR

METTE

RISK

ANALYSES

MEPO

Ris

k A

naly

sis

with N

etw

ork

-Reserv

oir S

imula

tion

Reserv

oir

to

Su

rface F

low

Mo

dellin

g

Field A North Sea

Fra

ctu

red H

orizonta

l W

ell

with N

etw

ork

Sim

ula

tion

Reserv

oir

to

Su

rface F

low

Mo

dellin

g

Field A with Fractured Horizontal Well

Fra

ctu

red H

orizonta

l W

ell

with N

etw

ork

Sim

ula

tion

Reserv

oir

to

Su

rface F

low

Mo

dellin

g

Horizontal Fractured Wells

Fra

ctu

red H

orizonta

l W

ell

Reserv

oir

to

Su

rface F

low

Mo

dellin

g

/ Power

Layout

Targets

Constraints

Boundary Cond.

Hold

up

Pigging

000 100 010 001 110 101 011 111 1011 0101

Valve positon

Time

Flow

Time

MEG/Artificial lift

Flow / Power

/ Potential flow

METTE

Field Simulator

Field Life Tracing

Reservoir

simulation

Reservoir

simulation

Motivation

Reserv

oir

to

Su

rface F

low

Mo

dellin

g

Simulation assited with the semi-analytical screening

Complex Time Consuming Simplified-Screening and fast

Questions

Reserv

oir

to

Su

rface F

low

Mo

dellin

g

• Fast and robust sreening tool for prognostic and diagnosis of fractures along the horizontal well

• Combaining the risk analysis with the network simulation

• Linking the semy-analytical model to the risk analysis software (commercial SW tool as MEPPO Schlumberger)

• Linking the semy-analytical model to a network model (METTE –Yggdrasil A/S, Oslo Norway)

Conclu

din

g R

em

ark

s

SU

MM

AR

Y

• Gudbrand Nerby, Ygddegrita A/S, Oslo Norway

• Ole Jacob Velle, Ygddrasil A/S, Oslo Norway • Asbjørn Sigurdsøn, Yggdrasil A/S, Oslo Norway

• Stefan Djupvik, SPT-Group, Kjeller Norway

• Amerada Hess, Copenhagen

• BP Amoco Stavanger

• Institut for Energy Technololgy IFE Kjeller Norway

Gotskalk Halvorsen, Jan Sagen

• Bayerngas Norge AS, Oslo Norway

Ack

now

ledg

men

ts

AC

KN

OW

LE

DE

NT

S

Questions

The Horizontal Fractured Well Model- Concluding Remarks

• A fast and robust algorithm is developed

• transient (SLAB model)

• basic depletion (BOX model)

• The bringing together of

• rate-time and

• pressure-time analyses

• The semi-analytical tool aids in

• optimizing the well production

• screening analysis

• the late-time approximations were verified

Model S

um

mary

Fracture Diagnosis Analyses

• Based on screening analysis of a horizontal well with fractures production data.

• Prognosis profiles can be generated and compared to the real observed data.

• Workflow for optimising number of fractures along a horizontal well in prognostic mode.

• Matching procedure combined with the risking tool features improves the fracture closure diagnosis.

• The semi-analytical model for a multiple-fractured-horizontal well, provides fast and robust features and can be used as screening tool for diagnostic and forecasting purposes.

Horizonta

Well

with F

ractu

res P

ossib

le E

xte

nsio

ns

optimising sweep in the north sea - owi apac 2018:...

Documents