Novel data interpretation

and active monitoring

methods for intelligent wells

Khafiz Muradov, Heriot-Watt U

Acknowledgments: D. Davies. R. Malakooti, F. Silva

Aberdeen, 22 Oct 2013

2/30

Introduction

I-well Monitoring Systems Review

Novel P and T Analysis in I-wells

Novel Active Monitoring Concept for I-wells

Outline

3/30

Value of Information

Remarks:

• Value of Information comes from zonal, phase flow rate values

as:

• Improved recovery: Well and field control basis

• Regulations met: Zonal flow rate values

• Reservoir model/understanding update

• Reduced risk

• Reduced number of interventions

• Value of information

Also:

• All (worldwide) case studies demonstrating the added value

from I-wells presume zonal, phase flow rates are known

Verification of

production objectives.

Uncertainty analysis

New field design?

Model

update

Production

control strategy

update

Advanced

well design

& modelling

courtesy Welldynamics

4/30 Advanced Monitoring Systems:

Number of Choices

Wellbore measurements can

provide a wide range of

information. Few examples:

• Temperature

DTS, FBG, ATS, PDG

• Pressure

PDG, FBG

• Acoustic signal

Seismic Array, FBG, DAS

• Density

Density-meter, FBG

• Flow rate

Venturi, Spinner

• Tracers

DTS, Multi-point T gauges

• Etc. Etc.

Applicable measurement

system greatly depends on the

installation and operational

capabilities

5/30

Monitoring Objectives for I-wells

Condition Monitoring

Well Performance

Well Stimulation

Flow Assurance

Advanced Completions Monitoring

Reservoir Characterization and Optimisation

Specific information is needed in each application

case!

6/30

Introduction

I-well Monitoring Systems Review

Novel P and T Analysis in I-wells

Novel Active Monitoring Concept for I-wells

Outline

7/30

Sensor Combinations: P &T are Key Elements

Type Application Distributed

Quasi-distributed/Discrete (limitations might be applied due to spatial

resolution)

T A V ε T P A S Q ε EM

Co

nd

itio

n

Mo

nit

ori

ng

Artificial lift

(operating GLV, ESP performance, etc.) √ √ √ √ √

Well/Pipeline Integrity (casing leak, flow behind casing,

packer isolation, etc.) √ √ √ √ √

Well/Pipeline Structural integrity (reservoir compaction,

formation movement, corrosion, etc) √ √

Well

Perf

orm

an

ce

Injection or production flow rate profiling √ √ √ √ √ √

Influx identification √ √ √ √ √ √ √

Sand production √ √ √

Gas/water/oil cut profiling √ √ √ √ √ √ √

Cross-flow between zones/layers √ √

Producing zone/layer identification √ √ √ √ √

Well

Sti

mu

lati

o

n

Perforated intervals identification √ √ √

Acidized intervals identification √

Well cleanup √ √ √

Hydraulic fracture (height/length/location identification) √ √ √

Flo

w

Assu

ran

ce

Slug flow monitoring √ √ √

Hydrates formation √

Ad

van

ced

Co

mp

l

eti

on

s

ICD/ICV/AICD performance monitoring √ √ √ √ √ √ √

ICV position √ √ √ √

Reserv

oir

Ch

ara

cte

riz

ati

on

Structural features

(faults, folds, etc) √ √

Boundaries √ √

Saturation Profiles √

Well test √ √ √

Ref: SPE 150159, SILVA, M. F. D., MURADOV, K. M. & DAVIES, D. R. 2012. Review, Analysis and Comparison of Intelligent Well Monitoring

Systems. SPE Intelligent Energy International. Utrecht, The Netherlands: Society of Petroleum Engineers.

8/30

Sensor Combinations: P &T are Key Elements

Ref: SPE 150159, SILVA, M. F. D., MURADOV, K. M. & DAVIES, D. R. 2012. Review, Analysis and Comparison of Intelligent Well Monitoring

Systems. SPE Intelligent Energy International. Utrecht, The Netherlands: Society of Petroleum Engineers.

Type Application Distributed

Quasi-

distributed/Discrete

T T P

Condition

Monitoring

Artificial lift √ √ √

Well/Pipeline Integrity √

Well/Pipeline Structural integrity

Well

Performance

Injection or production flow rate profiling √ √ √

Influx identification √ √ √

Sand production

Gas/water/oil cut profiling √ √ √

Cross-flow between zones/layers √

Producing zone/layer identification √ √

Well Stimulation

Perforated intervals identification √

Acidized intervals identification √

Well cleanup √ √ √

Hydraulic fracture (height/length/location identification) √

Flow Assurance Slug flow monitoring √

Hydrates formation

Advanced

Completions

ICD/ICV/AICD performance monitoring √ √ √

ICV position √

Reservoir

Characterization

Structural features (faults, folds, etc)

Boundaries

Saturation Profiles

Well test √ √ √

• Quantitative P&T interpretation methods are not available for many scenarios

• This reduces the sensor value; slows their development, installation and usage

9/30 Metrology, System’s Response,

Interpretation Uncertainty

Well/Reservoir Performance: Spatial,

Temporal (Alberts, Belfroid et al. 2007)

+ Signal’s Value

Sensor’s Performance

Robustness of Interpretation Methods

10/30

Introduction

I-well Monitoring Systems Review

Novel P and T Analysis in I-wells

Novel Active Monitoring Concept for I-wells

Outline

11/30

Common P&T Interpretation Practices

Discrete P sensors:

Well Testing

Production Analysis

Cross-correlation in flow meters etc.

Distributed T sensors:

• Continuous Testing: Flowing temperature

profile matched or Temperature gradient

analysis used

• Periodic Testing: Warm- or cool- back

temperature profile on shut-in

Little software available for

conventional temperature analysis

No software or analytical

methodology available for transient T

or T & P testing

P & T data are used separately

e wf

wf

T Tq

dT dz

Classical T interpretation

formula:

Zone 1

Zone 2

Zone 3

Layer-by-layer build-ups

12/30

Temperature Analysis has Many Applications

Qualitative:

• Temperature change

• Gas or Water breakthrough identification

• Operating GLV or casing leak

• Fracture height, scale deposition, other features

• Shut-in temperature change

• Cross-flow

Quantitative

• Thermal slug tracing

• Rate allocation:

• Temperature of mixed inflows

• Inverse modelling using wellbore temperature models

Other Temperature Interpretation Methods

13/30

Real-Time, Zonal Flow Rate Soft-Sensor

Measured values of Pressure, Temperature & total flow rate can be

used for real-time, zonal rate allocation if the model is properly

calibrated. The soft-sensor uses the advanced wellbore T-model

developed by (Muradov and Davies, 2008)

Zone 4 3 2 1

Inflow Control

Valve (ICV)

Sensor Module

Real-Time Measurement Positions of:

Upstream Pressure

Temperature

Downstream Pressure measurements

Pressure, bar

210

220

230

240

250

260

270

280

290

300

310

10/10/03 10/12/03 10/02/04 10/04/04 10/06/04 10/08/04 10/10/04 10/12/04

2d 3d 4d

1u 3u 4u

Rates, scm/d

0

1000

2000

3000

4000

5000

6000

10/10/03 10/12/03 09/02/04 10/04/04 10/06/04 10/08/04 10/10/04 10/12/04

Oil rate

Water rate

dGOR

Zonal P, T, well total flow rate

Zone 1 Zone 2 Zone 3 Zone 4

Oil rate, in situ

bopd

0 3,900 9,700 14,400

Gas rate, in situ

bgpd

0 7,800 2,200 0

Water rate, bwpd 0 0 11,200 8,800

Flow rate allocation

P & T profiles

Well model

Zonal phase flow rates

Ref: MURADOV, K. M. & DAVIES, D. R. 2009b. Zonal Rate Allocation in Intelligent Wells. EUROPEC/EAGE Conference and Exhibition. Amsterdam, The Netherlands: Society of Petroleum Engineers.

14/30

Advantages: Transient Temperature Analysis

Discrete or distributed, temperature

transient analysis is attractive:

• A new generation of down hole sensors has

become available: e.g. ATS, FBG

• Layer-by-layer testing is not required

• Tolerant of gauge drift & accuracy problems

• Differentiates zones

99.4

99.6

99.8

100

100.2

100.4

100.6

21/08/04 22/08/04 23/08/04 24/08/04 25/08/04 26/08/04 27/08/04

Tem

pera

ture

, C

Time

Zone 1

Zone 2

Zone 3

Zone 4

zonal temperatures - discriminate

215

225

235

245

255

265

21/08/04 22/08/04 23/08/04 24/08/04 25/08/04 26/08/04 27/08/04

Pre

ss

ure

, ba

r

Time

Zone 1

Zone 2

Zone 3

Zone 4

zonal pressures - indiscriminate

VS.

15/30

-0.65

-0.55

-0.45

-0.35

-0.25

-0.15

-0.050.0001 0.001 0.01 0.1 1 10 100

Time, days

BH

T c

ha

ng

e, K

analytical, before Tmin

numerical simulation

analytical, after Tmin

Flowing Temperature Change

1

2

3

TTA Workflow

Horizontal, Liquid Producing Well – Toe Section – Available

Interpretation of temperature transient allows estimating:

1. Thermal properties if rates and PIs are known (calibration mode) OR

2. Rates and PIs if thermal properties are known (soft-sensing mode)

Temperature Transient Analysis

Ref: MURADOV, K. & DAVIES, D. 2012b. Temperature transient analysis in horizontal wells: Application

workflow, problems and advantages. Journal of Petroleum Science and Engineering, 92–93, 11-23

16/30

220

225

230

235

240

245

250

255

260

265

270

22/08/04 23/08/04 24/08/04

Time

San

dfa

ce P

ressu

re,

bar

100.2

100.3

100.4

100.5

San

dfa

ce T

em

pera

ture

, C

toe zone sandface pressure

toe zone sandface temperature

A PDG, installed across a toe zone of a multi-zone, intelligent well, measures pressure and temperature:

215

225

235

245

255

265

21/08/04 22/08/04 23/08/04 24/08/04 25/08/04 26/08/04 27/08/04

Time

Pre

ss

ure

, b

ar

P 12

P 22

P 42

P 11

P 32

P 21

P 31

P 4199.4

99.6

99.8

100

100.2

100.4

100.6

21/08/04 22/08/04 23/08/04 24/08/04 25/08/04 26/08/04 27/08/04

Time

Tem

pera

ture

, C

T 12

T 22

T 32

T 42

T 11

T 21

T 31

T 41

Temperature Transient Analysis:

Example Application

The measurements at the toe zone are analysed first, with the analysis

further extended to the other sensors

zonal temperatures zonal pressures

17/30

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+07

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E+01

1.0E+02

1000 10000 100000

d(B

HP

i-B

HP

)/d

ln(e

lap

se

d tim

e),

ba

r /

ln(s

)

dB

HT

/dln

(ela

ps

ed

tim

e),

K / ln

(s)

elapsed time, s

zonal temperature derivative over logarithm of elapsed time

zonal pressure derivative over logarithm of elapsed time

1/2 slope

0 slope

1. Diagnostic, log-log plot of T recognises early-time regimes better than P,

2. P is more robust at later times

Diagnostic Plot: Both Pressure & Temperature Data required

Ref: MURADOV, K. & DAVIES, D. 2012b. Temperature transient analysis in horizontal wells: Application

workflow, problems and advantages. Journal of Petroleum Science and Engineering, 92–93, 11-23

18/30

-2000000

-1800000

-1600000

-1400000

-1200000

-1000000

-800000

-600000

-400000

-200000

0

0 50 100 150 200 250 300 350

square root of time in sec

pressu

re c

han

ge, P

a

toe zone pressure change

straight line trendline

-0.2

-0.18

-0.16

-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0 5000 10000 15000 20000 25000 30000

time from the beginning of drawdown, sec

tem

pera

ture

ch

an

ge,

K

toe zone temperature change

straight trend line

Initial P &T decrease Later T increase

Liquid rate is allocated: 78.0

12

wellofength

toeofength

ltyompressibi

coef

liqwell

toe

L

L

Сt

P

JTt

T

Q

Q

Linear flow regime

ETR

Pressure and Temperature Transient Analysis:

Zonal Contribution to Total Well Flow Rate

Ref: MURADOV, K. & DAVIES, D. 2012b. Temperature transient analysis in horizontal wells: Application workflow, problems and

advantages. Journal of Petroleum Science and Engineering, 92–93, 11-23

19/30

• Sandface T reconstructed using sensors installed downstream

• Interpretation started from the most upstream sensor

Multi-zone Interpretation Workflow

Ref: MURADOV, K. M. & DAVIES, D. R. 2012c. Temperature Transient Analysis in a Horizontal, Multi-zone, Intelligent Well. SPE Intelligent Energy International. Utrecht, The Netherlands: SPE

20/30

Introduction

I-well Monitoring Systems Review

Novel P and T Analysis in I-wells

Novel Active Monitoring Concept for I-wells

Outline

21/30

Multi-phase Soft-sensing

Measurement

Match

Measurement &

Estimation

Model → Estimate

Control ICV

setting

Well

Noise Previous Works (Passive Soft Sensing)

This work (Active soft sensing)

Dynamic Multi-phase Flow Model

• IPR Equation

• CPR Equation

• TPR Equation

• Welltest Equation

Manipulate ICVs to estimate

reservoir properties:

Reservoir Pressure, Well

Productivity Index, Water-Cut

& Gas Liquid Ratio

• Downhole Pressure

(steady-state or transient

measurements)

• Downhole Temperature

• Surface Flow Rates

22/30

Active Soft Sensing (Flow rate allocation in an n-zone I-Well)

Data

Estimation

Qsurface,

ΔPICV,

ΔPDrawdown

Measured

Data from n+1

ICV Settings

Pannulus,Ptubing,

T & Qsurface

Minimize

Mismatch

Estimate Reservoir

Parameters & Flow Rates

Predefined

Accuracy

Change

ICV’s

Setting

Reservoir parameters

assumed constant

during test period.

23/30

Case Study: A Three-Zone, Intelligent

Oil Producer

Integrated wellbore-reservoir transient simulation model

This study is on a triple zone I-well with two-phase (Oil & Water) flow

Rectangular reservoir with constant pressure boundaries

I-Well model (Annulus, ICV, Tubing) in OLGA connected to reservoir

Used a PVT file instead of a Black oil model

Zero skin

Zone-2

Zone-1

Zone-3

Coupled Rocx Simulator with

Wellbore Model in OLGA

24/30

Design Multi-Rate Tests

File: Case-Three Zone-Oil.tpl

PT [psia] (Zone-1) "Pressure"gfedcb PT [psia] (Zone-2) "Pressure"gfedcb PT [psia] (Zone-3) "Pressure"gfedcb

Time [d]

43.93.83.73.63.53.43.33.23.132.92.82.72.62.52.42.32.22.121.91.81.71.61.51.41.31.21.110.90.80.70.60.50.40.30.20.10

ps

ia

5800

5780

5760

5740

5720

5700

5680

5660

5640

5620

5600

5580

5560

No. of

Experiment

ICV1

(Open

Area

Fraction)

ICV2

(Open

Area

Fraction)

ICV3

(Open

Area

Fraction)

1 1 1 1

2 0 1 1

3 1 0 1

4 1 1 0

Only pressure transient data from build up tests analysed to avoid

the complexity generated by multi-layer reservoir well testing.

Shut

Zone-1

Shut

Zone-2

Shut

Zone-3

Surface Flow Rate Annulus Pressure Tubing Pressure

NO. EXP. ICV1 ICV2 ICV3 Qo Qw Pan1 Pan2 Pan3 Ptub1 Ptub2 Ptub3

1 1 1 1 - - - - - - - -

2 0 1 1 - - - - - - - -

3 1 0 1 - - - - - - - -

4 1 1 0 - - - - - - - -

25/30

Optimization of ICV Positioning (USS Pressure, SS Pressure & Flow Rate)

DC (simplex) technique selects ICV settings for new experiment

Experiment with least mismatch (white rows) removed as new

experiment updates the reservoir properties

ICV Positions

NO. of

EXP. ICV1 ICV2 ICV3

1 1 1 1

2 0 1 1

3 1 0 1

4 1 1 0

ICV Positions

NO. of

EXP. ICV1 ICV2 ICV3

1 1 1 1

2 0 1 1

3 1 0 1

4 1 1 0

5 0.5 0.5 1

ICV Positions

NO. of

EXP. ICV1 ICV2 ICV3

1 1 1 1

2 0 1 1

3 1 0 1

4 1 1 0

5 0.5 0.5 1

6 0.25 0.25 0.50

1 2

ICV Positions

NO. of

EXP. ICV1 ICV2 ICV3

1 1 1 1

2 0 1 1

3 1 0 1

4 1 1 0

5 0.5 0.5 1

6 0.25 0.25 0.50

7 0.52 0.32 0.25

4 Workflow gradually

approaches the

unknown reservoir

parameters via a

further multi-rate

experiment

5

ICV Positions

NO. of

EXP. ICV1 ICV2 ICV3

1 1 1 1

2 0 1 1

3 1 0 1

4 1 1 0

5 0.5 0.5 1

6 0.25 0.25 0.50

7 0.52 0.32 0.25

8 0.64 0.18 0.09

3

26/30 Two-Phase Flow Rates Allocation

(USS & SS Pressure & Flow Rate)

Zonal Productivity Index (Comparison between active soft-sensing method

& OLGA)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 1 2 3 4 5

PI, S

TB

/D/p

si

No. of Simplex

Zone-1

Estimated Value

True Value

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 1 2 3 4 5

PI, S

TB

/D/p

si

No. of Simplex

Zone-2

EstimatedValue

True Value

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 1 2 3 4 5

PI, S

TB

/D/p

si

No. of Simplex

Zone-3

Estimated Value

True Value

27/30 Two-Phase Flow Rates Allocation

(USS & SS Pressure & Flow Rate)

Zonal Water Cut (Comparison between active soft-sensing method

& OLGA)

0

0.05

0.1

0.15

0.2

0.25

0.3

0 1 2 3 4 5WC

, D

imen

sio

nle

ss

No. of Simplex

Zone-1

Estimated Value

True Value

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 1 2 3 4 5

WC

, D

imen

sio

nle

ss

No. of Simplex

Zone-2

EstimatedValue

True Value

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 1 2 3 4 5

WC

, D

imen

sio

nle

ss

No. of Simplex

Zone-3

EstimatedValue

True Value

28/30

Two-Phase Flow Rates Allocation

(USS & SS Pressure & Flow Rate)

Zonal Reservoir Pressure (Comparison between active soft-sensing method

& OLGA)

5750

5800

5850

5900

5950

6000

6050

0 1 2 3 4 5

Pre

ssu

re,

psi

No. of Simplex

Zone-1

EstimatedValue

True Value

5700

5750

5800

5850

5900

5950

6000

0 1 2 3 4 5

Pre

ssu

re,

psi

No. of Simplex

Zone-2

EstimatedValue

True Value

5640

5680

5720

5760

5800

0 1 2 3 4 5

Pre

ssu

re,

ps

i

No. of Simplex

Zone-3

EstimatedValue

TrueValue

Problem possibly

caused due to initial

guess in Excel Solver

29/30

Summary

Value of information in intelligent wells was

explained

Downhole sensors have been discussed

Transient P&T analysis has been shown to be

advantageous

Active soft-sensing, utilising i-well abilities to both

control and monitor separate production zones, has

been explained and its potential emphasized

30/30

“Added Value from Intelligent Well and

Field Systems Technology” JIP

http://www.pet.hw.ac.uk/research/iwfst/

iWFsT

Contact details:

David.Davies@pet.hw.ac.uk

Tel. 0131 451 3569

Khafiz.Muradov@pet.hw.ac.uk

Tel. 0131 451 4740

Institute of Petroleum Engineering

Heriot-Watt University, Edinburgh, UK, EH14 4AS

Contact Details

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