Primary funding is provided by

The SPE Foundation through member donations

and a contribution from Offshore Europe

The Society is grateful to those companies that allow their

professionals to serve as lecturers

Additional support provided by AIME

Society of Petroleum Engineers

Distinguished Lecturer Programwww.spe.org/dl

1

Society of Petroleum Engineers

Distinguished Lecturer Programwww.spe.org/dl

Lawrence CAMILLERIProduction Optimization Advisor

Artificial Lift Domain Head

lcamilleri@slb.com

Production Optimisation of Conventional &

Unconventional Wells with ESP Real Time Data

2

Unconventional Well Case Studies:

• Additional case studies for unconventional wells

are not included in this slide deck, however they

can be viewed via skype upon request to the

author, who can be reached at lcamilleri@slb.com.

• The skype session can also enable a more

detailed Q&A where required.

3

The Digital Transformation Is Growing Fast

Enablers:

▪ Declining costs of sensors and

data storage

▪ Rapid progress in advanced

analytics (e.g. machine learning)

▪ Greater connectivity of people

and devices

▪ Faster & cheaper data

transmission

4

Global Digital Oilfield Market ($bn)

Global Digital Oilfield Market 2017-2021

Technavio.com

Why ESP Production Optimisation?

▪ 940,000 producing wells worldwide

▪ 125,000 (13%) of these wells lifted by

ESP

▪ More than 54% of artificial lift global

spend is on ESP (4.8 B$ out of 8.8B$ in

2017) with all other types representing

less than 23% each.

▪ More than 30% of production lifted by

ESP as it is the lift type with the highest

rate

➔ ESP optimization has the biggest

impact on both production and AL

expenditure5

Power CablePump

Gas Separator

Motor

Pump Intake

Perforations

Motor

Seal

Gauge

VSD

Operator’s View

BP Technology Outlook: 2015

RossneftSPE 112238, Real Time Optimisation Approach for 15,000 ESP Wells

BP Believes that digital technologies can deliver

• 4% Production Enhancement

• 13% Cost Savings

▪ “Calculations showed economical efficiency of system implementation on 7451 wells out of over 11,000 working ESP wells, with possible annual oil production increase of 11 million bbls.”

▪ This justified the investment of 6400 US$ per well in automation hardware i.e. Variable Speed Drive, Gauges and real time data transmission

▪ The first step is identifying the well max. potential

▪ What is the actual IPR curve? Pr and PI?

▪ What could be the PI after possible stimulation?6

7

Commonly Available ESP

Real Time Data

5- Pump Discharge Pressure

6- Pi = Intake Pressure

7- Ti = Intake Temperature

8 - Tm = Motor Temperature

4- Tubing Head Pressure

1- Frequency

2- Current

3- Voltage

1. Uptime

2. Run Life

3. Power

Consumption

4. Production

Enhancement

VALUE OBJECTIVESGeneric Concepts:

▪ Gauge metrology

▪ Data visualization

▪ Slow & Fast Loop

▪ Data to Value Chain

▪ The importance of high frequency flowrate in addition to pressure data

Agenda

ESP Gauges…mature technology

8

1. Reliability – MTBF >10 years

2. No need for instrument line ➔low cost

3. Metrology of ESP gauges

▪ Accuracy +/- 5 psi

▪ Resolution 0.1 psi

▪ Drift 5 psi / year

➔INFLOW ANALYSIS IS POSSIBLE

Data Visualisation ➔ Solution = Multiple Tracks

Challenge = Large Production Sets- 6 to 15 analogue signals per well

- 1 million points / year /signal

Solution- Separate tracks for signal groups

- User defined filtering

9

Data to Value Chain

10

Execution Value

Domain

Information Decisions

Data

How Real Time Data & Automation

11

Proactive

Execution

Maximized

Value

Automation

Data

Information Decisions

Domain

The Value of Data…

12

Goal

Execute

Interpretation & Modeling

Evaluate Options

Data Acquisition

Decision Makers

Action takers

Shell’s Smart Fields Value Loop

Dedicated Field Stock:▪ New replacement to well-site with 12 hours▪ Dynamic to reflect future needs (not present)▪ Cover the range of expected flows and heads

Set Operating Parameters:1. Stop and Start2. Choke3. Frequency4. Control Mode

Monitor Performance:

Rate, pressure, temperature

Workover Ops

Design pump system

Pull Pump string

Workover Operations

(stimulation, fracking, sidetracking,

reperforation)

Carry Out failure analysis

Re-specify Equipment

ESP assembly & Commissioning

Stock is replenished via manufacturing

facilitySlow LoopTime is measured in months and in most

cases years

Fast LoopMinutes & hours

Slow & Fast Feedback Loops – Applied to ESPs

13

1.Uptime

2. Run Life

3. Power

Consumption

4. Production

Enhancement

VALUE OBJECTIVES

The Main Causes of ESP Downtime

The main two causes are:

1. Facility shut-downs e.g. electrical power interruption.

2. ESP stops automatically triggered by the motor controller to

protect the ESP from misoperation, which would otherwise lead

to failure. The main types are:

– Deadheading: Usually caused by inadvertent valve closure

– Pump-Off: Loss of submergence caused by pump rate greater

than inflow potential

– Gas Lock: performance is degraded by large volumes of free

gas, which initially leads to low flowrate events.

15

Improving Uptime with FAST LOOP – Gas Lock

16

Courtesy of SPE-190940-MS; Tuning VSDs in ESP

Wells to Optimize Oil Production—Case Studies

Frequency Varied Automatically

Constant Current Achieved

DataCurrent

Domain & Algorithm Sets- Target Current- Underload delay- Low Frequency Setting

ExecutionFrequency Variation

ValueEliminate All Trips

Uptime

74%

Intake Pressure Stabilised

All shutdowns eliminated for 300 days

(previously 1 shutdown per day)

200

400

300

500

2200

2000

1600

860

900

900

920

60

40

20

30

10

1800

We

llhe

adP

ress

.,p

sia

Fre

qu

en

cy, H

zC

urr

en

t, A

mp

sP

um

p I

nta

ke

Pre

ss.,

psi

aP

um

p D

isch

arge

Pre

ss.,

psi

aPeriod with no

flow to wellhead

Underload Trip

Setting 15 A

Current

Increasing

12:00 12:15 12:3011:4

5

Gas Lock Current = 20 A

Gas Lock Protection

18

PHI & Real Time Modelling Detecting GasFr

eq

uen

cy, H

z

4000

60

50

40

Q/Q

_BEP

PIP

& P

d, p

sia

2000

MayFeb Mar Apr June

PH

I

1.0

1.5

2.0

1.0

1.5

0.5

• When frequency is increased, there is a step decrease in discharge pressure, this is due to gas degradation

• PHI provides advanced warning of gas degradation, which is not detected by discharge pressure, which also provides an alarm as to when flowrate calculation overestimates rate

• Operating point relative to BEP has an impact on pump gas handling (SPE 163048)

• Frequency can be tuned to eliminate severe gas degradation.

• Also in constant current mode, when frequency is not “flat toping”, PHI is 1.0, thereby confirming gas degradation is quasi eliminated. When PHI is ~1.5, there is severe gas degradation

JanDecNov

0

0

Do

wn

ho

le

Pu

mp

Rat

e

• PCL pump flowrate is higher and the difference with measured rate provides a measure of gas degradation.

• PCL provides confirmation that transient downhole pump rate reaches zero rate when in gas lock mode.

Early identification of

gas degradation

Gas degradation

impact on rate

No “flat Toping” =

current target reached “flat toping” = current target

not reached = PHI = 1.5, PD

low

No “flat Toping” =

current target

reached = PHI=1.0

“flat toping” = current target not reached = PHI =

1.5

• PCL provides confirmation that transient downhole pump rate reaches zero rate when in gas lock mode.

• While this is OK occasionally to avoid a gas lock trip and motor overheating, the frequency of these events is high and causing additional stress to the ESP.

Increased Gas Degradation at Operating Point < BEP

Single Stage – Tulsa UniversityCourtesy of Gamboa, J. and Prado, M., 2012, Experimental

Study of Two-Phase Performance of an Electric-Submersible-

Pump Stage, SPE-163048-PA.

Multiple Stages – Schlumberger TestLess severe as the GVF is lower closer to discharge

of pump as gas is compressed

Challenge:

Low PI well with some

gas tripping on underload

causing downtime.

Solution

Constant intake pressure

feedback mode➔

maintains drawdown and

avoids tripping.

Constant intake pressure

Motor Temperature spikes during restarts

Frequency automatically varied

5 shut-downs in 5 days

Calculated liquid rate

100% Uptime with stable production for one year

Fre

qu

en

cy,

Hz

Mo

tor

Te

mp

, o

F

Inta

ke

pre

ss.,

psia

Liq

uid

Rate

,

B/D

22

Improving Uptime with FAST LOOP – Pump Off

Improving Uptime with SLOW Loop

At Workover Helico-Axial

Pump Installed

Wellhead Pressure

indicates Severe

SluggingStabilized

Production Increased by

250 sm3/day

23

SPE 141668; Helicoaxial Pump Gas Handling Technology: A

Case Study of Three ESP Wells in the Congo

Dedicated Field Stock:▪ New replacement to well-site with 12 hours▪ Dynamic to reflect future needs (not present)▪ Cover the range of expected flows and heads

Set Operating Parameters:1. Stop and Start2. Choke3. Frequency4. Control Mode

Monitor Performance:

Rate, pressure, temperature

Workover Ops

Design pump system

Pull Pump string

Workover Operations

(stimulation, fracking, sidetracking,

reperforation)

Carry Out failure analysis

Re-specify Equipment

ESP assembly & Commissioning

Stock is replenished via manufacturing

facilitySlow LoopTime is measured in months and in most

cases years

Fast LoopMinutes & hours

Slow & Fast Feedback Loops – Applied to ESPs

24

1. Uptime

2.Run Life

3. Power

Consumption

4. Production

Enhancement

VALUE OBJECTIVES

Measure, Classify and Record….➔ Slow Loop

❑ Run life improvement

requires continuous

monitoring and failure

analysis in the slow loop i.e.

years as opposed to minutes

❑ “Classification” is a key

enabler

❑ This principle can also be

applied to:

▪ Uptime Improvement

▪ Power Optimisation

▪ Production Enhancement

26

Slow Loop

“Implementation of a comprehensivemonitoring and failure investigation systemwas essential in the optimization of NorthKaybob ESP Performance” SPE 19379 – ESP

Improving Run Lives in the North Kaybob BHL Unit No 1, by C.G.Bowen and R.J Kennedy, Chevron Canada Resources.

Example of surveillance on a platform with 5 wells

Originator Quest Number Date

(of SQ Creation)

Well First Symptoms seen Classification

Code new

Classification Group Most Probable Cause

(Calculation conditions)

Paul McKay 20080303182656 Mar 03, 2008 LV 07 High Motor Temperature PE3 Procedure Error Site Hardware not correctly set-up (PAC/UniConn/ISP etc)

Leila Hamza 20080725165629 July 25, 2008 LV 07 Pressure Data GF4 Gauge Fault Values different from normal operations

Paul McKay 20080801231434 Aug 01, 2008 LV 07 High Motor Temperature LF1 Low Flow ESP running below minimum speed

Keith Rennie 20080918160320 Sept 18, 2008 Sinoe 311 Average amps PE3 Procedure Error Site Hardware not correctly set-up (PAC/UniConn/ISP etc)

Paul McKay 20081009081552 Oct 09, 2008 Sinoe 318 Intake Pressure IN5 Information ESP Running above normal production

Paul McKay 20081013170236 Oct 13, 2008 Sinoe 318 Intake Pressure LF1 Low Flow ESP running below minimum speed

Keith Rennie 20081014123107 Oct 14, 2008 Sinoe 318 Pressure Data GF4 Gauge Fault Values different from normal operations

John MacDonald 20081024191752 May 11, 2008 LV 07 Frozen Data GF3 Gauge Fault Short circuit in MDT

Stewart McIntosh 20081027162048 Oct 27, 2008 Sinoe 318 Intake Pressure DH3 Deadhead Partial Restriction above the ESP

Austin Mordi 20081101181305 Oct 31, 2008 Sinoe 311 Intake Pressure PO1 Pump Off Isolation valve closed below ESP

Keith Rennie 20081103025329 Nov 03, 2008 Sinoe 311 Intake Pressure DH3 Deadhead Partial Restriction above the ESP

Stewart McIntosh 20081110174651 Nov 10, 2008 T 1763 Review - Missing Data IN3 Information 11 point documentation (or part of) not sent

Keith Rennie 20081115172949 Nov 15, 2008 T 1763 Review - Missing Data IN3 Information 11 point documentation (or part of) not sent

Zi Qi Lian 20081117143505 Nov 17, 2008 Sinoe 318 Average amps PE3 Procedure Error Site Hardware not correctly set-up (PAC/UniConn/ISP etc)

Zi Qi Lian 20081117152928 Nov 17, 2008 T 1763 Temperature data GF5 Gauge Fault Failure/issue to Motor Temperature thermocouple

Zi Qi Lian 20081125162829 Nov 25, 2008 LV 01 Temperature data IN5 Information ESP Running above normal production

Zi Qi Lian 20081128101054 Nov 28, 2008 T 1763 Frozen Data DT2 Data Transmission No communication from Site - Frozen data

Keith Rennie 20081130211612 Nov 30, 2008 Sinoe 311 Temperature data IN4 Information ESP Running below normal production

Keith Rennie 20081130220033 Nov 30, 2008 Sinoe 316 Pressure Data IN5 Information ESP Running above normal production

Zi Qi Lian 20081221163235 Dec 15, 2008 Sinoe 318 Pressure Data PE1 Procedure Error Client operators not following recognized procedures

John MacDonald 20090105120308 Jan 05, 2009 Sinoe 318 Intake Pressure PE1 Procedure Error Client operators not following recognized procedures

Austin Mordi 20090109003453 Jan 07, 2009 Sinoe 318 Intake Pressure DH3 Deadhead Partial Restriction above the ESP

Stewart McIntosh 20090116164551 Jan 16, 2009 Sinoe 318 Intake Pressure IN4 Information ESP Running below normal production

Paul McKay 20090131092050 Jan 31, 2009 Sinoe 318 Average amps SE1 Surface Electrical High/Low Voltage supply power issue

Keith Rennie 20090204121206 Feb 04, 2009 LV 01 High Motor Temperature PO1 Pump Off Isolation valve closed below ESP

Austin Mordi 20090205030928 Feb 05, 2009 LV 07 Intake Pressure PO1 Pump Off Isolation valve closed below ESP

Keith Rennie 20090205111028 Feb 05, 2009 Sinoe 318 Pressure Data PO1 Pump Off Isolation valve closed below ESP

Keith Rennie 20090205173202 Feb 05, 2009 LV 01 VSD Input Voltage SE1 Surface Electrical High/Low Voltage supply power issue

Austin Mordi 20090302192416 Mar 02, 2009 Sinoe 316 Pressure Data IN4 Information ESP Running below normal production

Paul McKay 20090322211608 Mar 22, 2009 Sinoe 311 Frozen Data GF2 Gauge Fault Short Circuit above MDT

Austin Mordi 20090329111806 Mar 29, 2009 Sinoe 316 Pressure Data IN4 Information ESP Running below normal production

Stewart McIntosh 20090405052033 Apr 04, 2009 Sinoe 316 Pressure Data GF7 Gauge Fault ISP/PIC card errors

Zi Qi Lian 20090407114109 Apr 06, 2009 LV 01 Site Hardware not configured PE3 Procedure Error Site Hardware not correctly set-up (PAC/UniConn/ISP etc)

Keith Rennie 20090410090031 Apr 10, 2009 Sinoe 311 Intake Pressure PO2 Pump Off Reduction to Inflow (PI dropping)

Paul McKay 20090417193122 Apr 17, 2009 Sinoe 311 Review - Missing Data IN3 Information 11 point documentation (or part of) not sent

Paul McKay 20090419024008 Apr 19, 2009 Sinoe 318 Frozen Data GF3 Gauge Fault Short circuit in MDT

Zi Qi Lian 20090421142017 Apr 21, 2009 LV 07 VSD Input Voltage SE1 Surface Electrical High/Low Voltage supply power issue

Paul McKay 20090504081449 May 03, 2009 All Sinoe Frozen Data DT2 Data Transmission No communication from Site - Frozen data

Stewart McIntosh 20090527024149 May 27, 2009 All Sinoe Review - Missing Data IN3 Information 11 point documentation (or part of) not sent

Paul McKay 20090615022012 Jun 15, 2009 Sinoe 311 Pressure Data IN4 Information ESP Running below normal production

Keith Rennie 20090620005912 Jun 19, 2009 LV 01 High Motor Temperature DH3 Deadhead Partial Restriction above the ESP

Keith Rennie 20090620014623 Jun 20, 2009 LV 07 High Motor Temperature DH3 Deadhead Partial Restriction above the ESP

Stewart McIntosh 20090713114554 Jul 04, 2009 Sinoe 311 Pressure Data IN5 Information ESP Running above normal production

Paul McKay 20090711191010 Jul 11, 2009 Sinoe 318 Frozen Data GF7 Gauge Fault ISP/PIC card errors

Madjiid Remita 20090719150440 Jul 17, 2009 OR-7 Frozen Data DT2 Data Transmission No communication from Site - Frozen data

Stewart McIntosh 20090728005730 Jul 24, 2009 Sinoe 311 Frozen Data GF7 Gauge Fault ISP/PIC card errors

Stewart McIntosh 20090728013600 Jul 26, 2009 OR-7 Frozen Data DT2 Data Transmission No communication from Site - Frozen data

Keith Rennie 20090730093053 Jul 30, 2009 Sinoe 316 Pressure Data GF4 Gauge Fault Values different from normal operations

Keith Rennie 20090815015118 Aug 14, 2009 Sinoe 318 Pressure Data DH3 Deadhead Partial Restriction above the ESP

Lawrence Camilleri 20090923221827 Jul 01, 2009 OR-7 Pressure Data DH3 Deadhead Partial Restriction above the ESP

Lawrence Camilleri 20090923223018 Jul 01, 2009 OR-7 VSD Input Voltage SE1 Surface Electrical High/Low Voltage supply power issue

Lawrence Camilleri 20090923220645 Jul 09, 2009 OR-7 High Motor Temperature DH3 Deadhead Partial Restriction above the ESP

Lawrence Camilleri 20090923213758 Jun 30, 2009 OR-7 Average amps PE3 Procedure Error Site Hardware not correctly set-up (PAC/UniConn/ISP etc)

Paul McKay 20090906035749 Sep 06, 2009 Sinoe 318 Frozen Data GF3 Gauge Fault Short circuit in MDT

Keith Rennie 20090909015257 Sep 09, 2009 Sinoe 316 Pressure Data GF4 Gauge Fault Values different from normal operations

Stewart McIntosh 20090916190314 Sep 16, 2009 OR-7 Review - Missing Data IN3 Information 11 point documentation (or part of) not sent

Keith Rennie 20090926103105 Sep 19, 2009 LV 01 Pressure Data DH3 Deadhead Partial Restriction above the ESP

Stewart McIntosh 20090921222148 Sep 20, 2009 OR-7 Frozen Data DT2 Data Transmission No communication from Site - Frozen data

Elshan Hasanov 20091010133142 Oct 10, 2009 Sinoe 318 Pressure Data DH3 Deadhead Partial Restriction above the ESP

Paul McKay 20091015095916 Oct 15, 2009 Sinoe 316 Pressure Data DH3 Deadhead Partial Restriction above the ESP

Paul McKay 20091018120627 Oct 18, 2009 Sinoe 316 Average amps IN4 Information ESP Running below normal production

Keith Rennie 20091024105843 Oct 24, 2009 OR-7 Pressure Data DH3 Deadhead Partial Restriction above the ESP

Paul McKay 20091027232646 Oct 27, 2009 OR-7 Intake Pressure DH3 Deadhead Partial Restriction above the ESP

Paul McKay 20091102042146 Nov 02, 2009 Sinoe 318 Pressure Data IN6 Information Temporary step change in Pi and Pd with both parameters folowing the same trend (typical of change in surface process)

Ricky Rae 20091108173929 Nov 07, 2009 OR-7 Pressure Data DH3 Deadhead Partial Restriction above the ESP

Paul McKay 20091112092134 Nov 11, 2009 OR-7 Pressure Data DH3 Deadhead Partial Restriction above the ESP

Lawrence Camilleri 20091118131435 Nov 11, 2009 OR-7 VSD Input Voltage SE3 Surface Electrical Loss of input power/voltage

Stewart McIntosh 20091114023402 Nov 12, 2009 Sinoe 316 Pressure Data GF4 Gauge Fault Values different from normal operations

Paul McKay 20091113103810 Nov 13, 2009 Sinoe 318 Pressure Data IN5 Information ESP Running above normal production

Keith Rennie 20091124035736 Nov 24, 2009 Sinoe 318 Pressure Data DH1 Deadhead Restriction above the ESP (Up-stream of flowline pressure indicator)

Database of stress creating events

Recording & Classification

27

Identify Deadheads as recurring event

Investigate root cause

& implement remedial action

enabled by

CLASSIFICATION

Run-Life Improvement powered by data

29

FAST LOOP; Infant Mortality

Half Year Survivability Improved

from 78% to 85%

SLOW LOOP;

Five-year Survivability

Improved from 15% to 30%

SPE 134702 - How 24/7 Real-

Time Surveillance Increases

ESP Run Life and Uptime

Survival Analysis VS start frequency

10/18/2019 Fabien PEYRAMALE

This study shows that reducing the number of stop/starts not only

improves uptime/production but also improves run life

30

Run Life – Recommended Measurement Technique

▪ Survival Analysis considers pumps still running ➔ leading indicator

▪ Kaplan Meier Presentation removes dependency on changing demography.

▪ Exponential fit assumes constant survival rate, which is useful as one can identify populations above and below the average.

31

𝑹(𝒕) = 𝒆−𝒕

𝑴𝑻𝑩𝑭

This mathematical model is

based on a constant survival

rate. (a.k.a. single parameter

Weibul Model).

Convenient as it allows the user

to identify, which groups of

pumps have a higher or lower

than average run life. Also easy

to integrate and manipulate in

excel as

𝑴𝑻𝑩𝑭 = න𝟎

∞

𝒆−𝒕

𝑴𝑻𝑩𝑭 𝒅𝒕

Illustration of Shape factor “b”

• All these survival curves

have the same MTBF,

chosen arbitrarily as 8

years to illustrate the shape

factor concept.

• Low shape factor will have

increased infant mortality.

• High shape factor will have

reduced infant mortality.

R(t) = 𝒆−𝒕

𝒂

𝒃

If it is not possible to model using a classic

exponential function which assumes a constant failure

rate. A shape factor is required (=b), which is also

known as a 2 parameter Weibull function.

Types of Analysis that can be performed

Time Lapsed Analysis shows improvement in MTBF

Courtesy of SPE Paper 134702

❑ Single Section Motor in Low

Temperature (~50 to 75 oC) –

MTBF = 4.9 years

❑ All Wells – MTBF = 3.2 years

❑ Tandem Section Motor in Higher

BHT (~100 oC) – MTBF = 2.4

years

Comparing Population Types; Here we prove that

temperature impacts average run life but has a reduced

effect on infant mortality

• Time lapsed to monitor evolution of run life

• Comparing population sub-sets to identify cause of reduced overall run life

33

1. Uptime

2. Run Life

3.Power

Consumption

4. Production

Enhancement

VALUE OBJECTIVES

Power Optimisation ➔ Minimise PF x EFF

Voltage 100%(Voltage Range from 0.7 to 1.2

of nominal)

746

3PFIVPower

=

EFF

V Voltage

I Current

PF Power Factor

EFF Efficiency

The power equation shows that

for a given pump load, when the

(PFxEFF) is maximized, the

current is minimized.➔ Minimise Power consumption

➔ Minimise Motor Temperature, which

maximises run life

This is achieved by controlling

voltage35

01

-May

02

-May

03

-May

04

-May

05

-May

Reduction in voltage:

▪ 38% power saving

(156 KVA vs 248 KVA)

▪ Run Life Increase

• Motor Temperature

reduction

• Motor Voltage

reduction

Achieved without any loss

in production

ESP Power Optimisation ➔ Double Benefit

36

Current Reduced by 14 A

Motor Temperature

Reduced by 10 F

Constant Production rate

Voltage Reduced by 250 V

Cost Reduction – Field Wide Energy Saving Examples

▪ Industry pay-back period on

energy savings is between 0.8

& 1.2 years with savings of 30

M$ in 2010 (source IAC 2017)

▪ For ESP wells, the pay-back

period is zero as real time

data is available anyway

37

▪ 24 wells

▪ Total Power Cost = 2.4M$ / month

▪ Saving= 380k$/month = 14%

Stress Caused by Temperature & Voltage

Effect of Conductor Temperature

Arrhenius Law

Effect of Voltage

38

1. Uptime

2. Run Life

3. Power

Consumption

4.Production

Enhancement

VALUE OBJECTIVES

The Value of High Frequency Flowrate & Pressure

Without Build-ups

Liquid Rate

Pressure

Downhole Measurement with:- High Frequency- High Resolution- High Repeatability

IPR

on the fly

Reserves &

Drainage

area

evolution

Reservoir

Flow regime

(e.g.

Fractures)

Skin and

depletion

evolution

PTA in

drawdown

Fast Loop Slow Loop40

Flowrate trends….

Liquid rate from test separator

Frequency 1/month

Accuracy ±5%

Resolution ?

Repeatability Poor

Discernible Trend

NO

▪High frequency▪High resolution▪High repeatability

Discernible Trend

Accuracy not

required for a trend

41

Test SeparatorLiquid rate from test separator

Calculated using real time data

Frequency 1/month 1/min

Accuracy ±5% same

Resolution ? <100 bbl/d

Repeatability Poor HIGH

Discernible Trend

NO YES

No need for additional hardware

1.Proven from first principles ➔ Always true

2.Equation is density independent:

▪ Valid with changing WC

▪ Can handle phase segregation

3.Analytical equation ➔ derivative provides

resolution

4.Type Curve ➔ Shape does not change

with calibration.

5.Valid across the full range of the pump

curve irrespective of pump type.

42

Do

wn

ho

le L

iqu

id R

ate

Uncalibrated

Calibrated

time

One Possible Method …

URTEC 2475126 - ESP Real-Time Data Enables Well Testing With High

Frequency, High Resolution, and High Repeatability in an Unconventional Well

( )

746

3

58847

=

− EFFPFIVQPP

p

pid

Pump-absorbed Power = Motor-generated Power▪ High frequency▪ High resolution▪ High repeatability

IPR on the fly without build-ups

Liquid Rate

Pwf = Flowing Pressure

Wellhead Pressure

Frequency

Enabled by flowrate and pressure with:

• High frequency = 1/min

• High resolution = <20 rbpd

• High repeatability

SPE paper 183337

Small changes in frequency and wellhead

pressure provide multi-rate test

opportunities

IPR Measurement on the fly

March 2015 May 2016

TIME LAPSED ANALYSIS1. PI increase ➔ no skin increase

2. Depletion is circa 145 psi over this

14 month period using SIP

technique

Liquid Rate

Pwf = Flowing Pressure

Wellhead Pressure

Frequency

44 SPE paper 183337

PTA (Pressure Transient Analysis) in drawdown ▪ Problem Statement:

– PTA has a huge value for skin and boundary characterization, however traditionally requires build-ups due to lack of downhole transient rate.

– However build-ups are associated with production deferment.– Also build-ups are rarely long enough to see boundaries.

▪ The challenge: Drawdown PTA is mathematically equivalent to build-ups, however presents the following challenges:– Requires transient flowrate– High resolution ESP gauges ➔ Noisy derivative– Slugging wells

▪ The Solution: – Virtual flowmetering can provide downhole flowrate with high frequency and resolution– As flowrate is measured at the same point as pressure, there is no time lag between flow

and pressure measurements.– Superposition time is enabled and provides natural smoothing– Drawdown means that one can wait to see boundary conditions.

45

A common Problem

Derivative is too noisy in drawdown to identify IARF

IARF identified using semi-log with Superposition time

Obtain history match and measure:

- Skin

- Distance to constant pressure

boundary

Why Skin is independent of absolute rate

• As absolute rate changes,

permeability changes linearly with

rate (m), but skin does not change!

• Skin is independent of rate

accuracy and only dependent on

rate trend.48

Measuring Skin with Drawdown

49

900

1300

Pressure [psia

]

3 4 5 6 7 8 9 10 11 12 13

Time [hr]

1300

1400

Dow

nhole

rate [B

/D

]

- High frequency- High resolution- High repeatability

Skin = intercept / slope = 6.4

IARF

Example from SPE paper 185144 shows how rate data enables use of semi-log.

This enables correct identification of IARF (Infinite Acting Radial Flow) and

therefore skin measurement.

Superposition TimeP

res

su

re, p

si

TimeDo

wn

ho

le R

ate

, B

/DP

res

su

re,

psia

The value of stimulation

Before Stimulation After Stimulation

Skin 6.4 0

PI 1.31 BPD/psi 2.21 BPD/psi

Pwf 740 psia

Liquid Rate 1232 BPD 2089 BPD

Incremental Liquid Rate 0 857 BPD

WC 95% 95%

Incremental Oil Rate 0 43 BOPD

Incremental Annual Revenue @ 50 US$/bbl

0 782 k$

Even where WC (Water Cut) is high and the absolute rate

is relatively low, the value of skin remediation is high

50

51

• Example from SPE paper 176780

• Additional example in SPE paper 181663▪ Challenge:– Monitor evolution of reservoir

pressure & skin without build-ups.

– Apply to wells with large number of

stop/starts

▪ The Solution: History Matching but

with the use of downhole flowrate which

has high frequency and resolution.

▪ Value Statement– Find the maximum rate that the well

can produce without depletion

– Monitor evolution of drainage area

static pressure and skin

– Avoid downtime associated with

build-ups.

– Avoid the need for observation wells

Period #2Depletion = 0.3 psi/day

Skin Pressure drop 150 psi

Period #1Depletion = 1.25

psi/dayNo skin change

High Frequency Rate

Flowing

Pressure

Pre

ssu

re, p

sia

Ra

te, B

/D

Depletion & Skin Monitoring

Before & After History Matching

Because of high frequency rate capturing transients, the match can be

achieved even where the well experiences numerous shut-downs

Before History Matching After History Matching

High Frequency Rate

Flowing

Pressure

Reservoir Pressure

Flow Regime Identification

Bilinear

¼ slope

Boundary

Dominated

unit slopeLinear

1/2 slope

Unloading Casing

By plotting the rate normalized pressure difference versus time in log-log ➔ Reservoir flow regimes can be identified...

• Optimise Frac Design

• Drainage Area & Reserve Estimates

Example of a

MFHW (Multiple

Fractured Fracked

Horizontal Well

courtesy of Paper

URTEC #2471526

53

High Frequency vs. Test Separator Flowrate

One cannot see transient fracture flow regimes with low frequency &

low repeatability test separator data, even daily data is insufficient.

54

BDF ➔ Reserves & PI

𝑷𝒊 − 𝑷𝒘𝒇

𝒒=

𝟏

𝑪𝒕 × 𝑵𝒕𝒎𝒃 + 𝒃𝒑𝒔𝒔

Boundary Dominated Flow

Slope = 1/(Ct x N) = 0.006118 psi/barrels

PI = 0.81 bpd/psi

Test Separator does

not capture BDF

• 𝑡𝑚𝑏 =𝑁𝑝

𝑞= 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝑏𝑎𝑙𝑎𝑛𝑐𝑒 𝑡𝑖𝑚𝑒

• 𝑏𝑝𝑠𝑠 =1

𝑃𝐼= inverse of PI for pseudo

steady-state condition

• Ct = compressibility, 1/psi

• N = Pore Volume (Liquid in place),

barrels

• Blasingame, T.A. and Lee, W.J., 1986,

Variable-Rate Reservoir Limits testing,

presented at the Permian Basin Oil &

Gas Recovery Conference held in

Midland, TX March 13, 1986, SPE 15028

Example from SPE paper 185144

55

HCPV (Hydrocarbon Pore Volume) & Drainage Area

56

• Monitor well drainage area reservoir pressure and evaluate impact of:• Drawdown• Production rates of offset

wells

• In this example, HCPV is increasing, which suggests that the well recovery factor is increasing.

• Ct = compressibility

• N = HCPVCase Study SPE

paper 183337

Value = Decisions

58

IPR

on the fly

Reserves &

Drainage area

evolution

Reservoir Flow

regime (e.g.

Fractures)

Skin and

depletion

evolution

PTA in

drawdown

Skin / Stimulation Candidates

Water Injection Management

Fracture Design Optimisation

Infield Drilling Placement

Pump Sizing

Drawdown Management

Data

1. Openhole logs

2. Geological maps

3. PVT data

4. Permanent Gauges (P,

T, Q, etc…)

5. Rate history

6. Cased Hole (PLT,

RST)

7. Build-ups

Information

1. Equipment health check

2. IPR curve3. Skin4. Pr trend5. Recovery factor6. Scale Treatment7. Etc…

Real-time Data

Static, Contextual

Data

Episodic DataData which does not stream

automatically in real-time

and requires intervention

Data to Information Map

Analytical Tools

◼ Equipment Models

◼ NODAL

◼ Pressure transient

◼ Rate transient

◼ Simulation

◼ Lab analysis

◼ Etc…

Will episodic data become a thing of

the past to be replaced by high

frequency data available on all wells,

especially for flowrate?

Digital Twins

Virtual Flowmeters

59

Key Take-AwaysReal-Time Data drives Production Optimisation

1. Uptime and MTBF: Improved by identifying the cause of trips & implementing remedial action:▪ FAST LOOP = choke, pump speed and/or control settings▪ SLOW LOOP = Changing operating procedures and/or ESP design

2. Power Optimisation: Field average power savings of up to 20% are documented with individual case studies showing 40% saving.

3. Production Enhancement:▪ Key Enabler = high frequency / high resolution liquid rates & pressure data▪ Value =

▪ IPR Curve on the fly ➔ Establish well potential and update on a regular basis▪ PTA – Identify skin ➔ Stimulation candidates▪ History Matching ➔ evolution of reservoir pressure & skin▪ Flow Regime ➔ fracture protocol Design▪ Monitor evolution of drainage area

60

Bibliography• Bowen, C.G. and Kennedy, R.J., 1989. Electric Submersible Pumps Improving Run Lives in the North Kaybob BHL Unit No.1, Paper SPE 19379.

• Blasingame, T.A. and Lee, W.J., 1986, Variable-Rate Reservoir Limits Testing, presented at the Permian Basin Oil & Gas Recovery Conference, held in Midland, Texas, 13 March. SPE 15028

• Camilleri, L., Brunet, L., and Segui, E. 2011. Poseidon Gas Handling Technology: A Case Study of Three ESP Wells in the Congo. Presented at the SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 6–9 March. SPE 141668-MS.

• Camilleri, L. and MacDonald, J., 2010, How 24/7 Real-Time Surveillance Increases ESP Run Life and Uptime, SPE 134702 presented at ATCE in 2010 in Florence, Italy.

• Camilleri, L., El Gindy, M., and Rusakov, A. 2015. Converting ESP Real-Time Data to Flow Rate and Reservoir Information for a Remote Oil Well. Presented at the SPE Middle East Intelligent Oil & Gas Conference & Exhibition, Abu Dhabi, UAE, 15–16 September. SPE-176780-MS.

• Camilleri, L., El Gindy, M., and Rusakov, A. 2016. ESP Real-Time Data Enables Well Testing with High Frequency, High Resolution, and High Repeatability in an Unconventional Well. Presented at the Unconventional Resources Technology Conference, San Antonio, Texas, USA, 1–3 August 2016. URTEC 2471526.

• Camilleri, L., El Gindy, M., and Rusakov, A. 2016. Providing Accurate ESP Flow Rate Measurement in the Absence of a Test Separator presented at the SPE Annual Technical Conference held in Dubai, UAE, 26 – 28 September 2016, SPE – 181663 – MS

• Camilleri, L., El Gindy, M., and Rusakov, A. 2016. Testing the Untestable... Delivering Flowrate Measurements with High Accuracy on a Remote ESP Wellresented at the Abu Dhabi International Petroleum Exhibition and Conference held in Abu Dhabi, UAE, 7–10 November 2016 – SPE-183337-MS

• Camilleri, L., El Gindy, M., and Rusakov, I. et al. 2017. Increasing Production with High-Frequency and High-Resolution Flow Rate Measurements from ESPs. Presented at the SPE Electric Submersible Pump Symposium, The Woodlands, Texas, USA, 24–28 April 2017. SPE-185144-MS.

• Camilleri, L., and G. Hua, N. H. Al-Maqsseed and A. M. Al Jazzaf, 2018, Tuning VSDs in ESP Wells to Optimize Oil Production—Case Studies, SPE 190940

• Lejon, K.; Hersvik, K.J.; Boe, A; 2010, Multi-asset Production Support Center – Generating Values, paper SPE 127730 presented at the SPE Intelligent Energy Conference and Exhibition held in Utrecht, the Netherlands, 23-25 March 2010.

• Nieto, A., Brinez, D., Lopez, J.E., Marin, P., Cabrera, S., Paya, D., Fernandez, L., Villalobos, J., Cifuentes, E., 2017 Electrical Cost Optimization for Electric Submersible Pumps: Systematic Integration of Current Conditions and Future Expectations. SPE 184006

• Yero, J. and Moroney, T.A., 2010, Exception Based Surveillance, SPE 127860, 2010

• Zdolnik, S., Pashali, A., Markelov, D., Volkov, M., 2008, Real Time Optimisation Approach for 15,000 ESP Wells, SPE 112238

Society of Petroleum

Engineers Distinguished

Lecturer Programwww.spe.org/dl

62

Your Feedback is

Important

Enter your section in the DL Evaluation

Contest by completing the evaluation

form for this presentation

Visit SPE.org/dl

62

Real-Time Data can truly drive

Production Optimisation

BACK-UP SLIDES

UPTIME: Measurement, Diagnostic, Resolution

Automation Delivers Value

65

High

Frequency

Flowmeter

Well Model

VSD Control

Data

Digital Twin

Execution

35 psia

Flowing

Pressure

Without

Pump-Off

60%

MET

HA

NE

PR

OD

UC

TIO

N

Increased PCP run life

SPE 181218

Using Liquid Inflow Method to Optimize

Progressive Cavity Pumps

Fast Loop

Automatic Feedback

Loop

Proactive

Example of how uptime can be measured

Statistically, more than

3 starts per month has

a negative impact on

run life (see back-up

slide for explanation)

Highest Frequency of stop/start

with 10 restarts in 15 days

Continuous measurement provides the evolution of uptime,

this is made possible by high frequency real time data.

Operational Days 514

Downtime Days 46

Uptime 91%

Number of stops 108

Stops / month 6.4

66

1 2 31a

Explanation

1 Following a shut-down there is phase

segregation in the well and ESP produces

nearly 100% water for the first 1.5 hours.

This is corroborated by the high discharge

pressure and current.

This is also seen on the intake

temperature as the pump is 1600ft TVD

above the top of perforations. Therefore

initial measured temperature is a function

of geothermal gradient and when wellbore

storage is finished, then the gauge sees

100% reservoir fluid which is a few

degrees hotter.

1a This is the pump up time. It takes

approximately 30 min for the pump to

reach the discharge pressure required to

lift fluid to the wellhead

2 Pumping 100% reservoir fluid. Discharge

pressure is declining as the column

becomes lighter with increasing GOR.

3 Free gas in the pump causes head

degradation and therefore a drop in

discharge pressure. This leads to a

reduction in flowrate which is seen with an

increase in intake pressure and a

reduction in current. The pump finally

trips on underload, but in any case would

not have been able to lift fluid to surface

as discharge pressure falls below 1500

psia. 6 pm 9 pm

200

400

600

3000

1000

800

900

1000

160

60

165

20

40

2000

40

Fre

quency,

Hz

Curr

ent,

Am

ps

Pum

p Inta

ke

Pre

ss.,psia

Pum

p

Dis

charg

e

Pre

ss.,psia

Tubin

g

Head

Pre

ss., p

sig

0

20

0

Inta

ke

Tem

p,

deg

F

67

Resolution both in the “fast” loop

▪ Fast Loop SolutionEliminate trips through diagnostics (aka

Root Cause Analysis) and remedial action:

– Choke setting change

– Speed change

– Or operating the ESP on a feedback loop

using a VSD to maintain either constant

current or intake pressure.

– Or defeating traditional current underload

and only shutting down on motor

temperature (previously not possible

before the advent of ESP gauges)

▪ Slow Loop SolutionThese observations are subsequently used

in the redesign of the ESP (see example in

back-up slides of addition of helicoaxial

pump which eliminates slugging).

Schlumberger Case Study from Kazakhstan shows 27% downtime reduction

Paper by OXY Permian and Schlumberger

68

Run Life: Measurement, Diagnostic, Resolution

Tracking of “stress” events

70

KPI = number of critical

events per well:

• This will initially rise as

population grows

• Will eventually drop if

root cause analysis and

remedial action is

performed in both fast &

slow loops

Reduction as

remedial action

implemented

Courtesy of Schlumberger

Surveillance Center

Tracking of “stress” events• Criticality drives prioritisation of:

– Root cause analysis

– Remedial action

• Identify recurring events

• Below is the classification suggested by SPE paper 134702 and results from surveillance of over 200 wells over a period of 6 years

KPI = number of critical events per well:

• This will initially rise as population grows

• Will eventually drop if root cause analysis and remedial action is performed in both fast & slow loops

Courtesy of Schlumberger Surveillance Center

Reduction as remedial

action implemented

71

Power: Measurement, Diagnostic, Resolution

Production Enhancement

❑The main objectives are:▪ Increase drawdown▪ Reduce skin▪ Maximise drainage area i.e. recoverable

reserves

❑The levers available are:▪ Manage drawdown (choke and pump speed)▪ Stimulate to remove skin▪ Manage pressure support e.g. water injection

❑Constraints are understanding the well potential in terms of:▪ Inflow Performance▪ Pressure support▪ Well interference▪ Skin – formation damage▪ Reservoir size (boundaries)

Testing is essential to:

- Determine well

potential

- Take the right action

(use the right lever) to

increase production

73

Digital Twins…

DEFINITION = Mathematical model which is a replica of a physical asset and is automatically and continuously updated using real time data.

Key properties for successful digital twins:

➢ Single calibration valid for long periods of time otherwise model updates are laborious & costly. This requires algorithms which have self calibration features e.g. Specific Gravity independent to handle changes in WC and GOR

➢ Valid across a wide range; therefore make maximum use of analytical models which respect physics at all times and therefore always true as opposed to correlations or artificial intelligence which are only valid once trained / calibrated.

➢ Avoid algorithms with iterations required to resolve unknowns as these are time consuming. There is also the risk of non-convergence.

Digital Twin

To deliver “Proactive Execution”

consistently and effectively on a large

scale, digital twins are indispensable

74

On the fly IPR with Multirate Test, SPE 185144

75

2600

2800

1680

3000

1660

1640

1620

Liq

uid

Rat

e, r

bp

dES

P In

take

Pre

ssu

re,

psi

aFr

eq

ue

ncy

, H

zTu

bin

g H

ead

P

ress

ure

, psi

a

Day 1 Day 7 Day 14

▪ IPR is obtained on-the-fly without a build-up to measure static pressure.

▪ Made possible by liquid rate with high frequency, high resolution and high repeatability.

▪ Difficult with test separator data.

PI = 14 rbdps/psi

Pr = 1856 psia

5 Hz

150 psi

ESP Condition Monitoring - Digital Twin Example

Digital Twin provides

A. Real time High Frequency

Flowrate

B. Real Time ESP Management

– Health Monitoring

– Operating Point

– Power Optimisation

Engine is located in the cloud with

the following advantages:

▪ Collaborative workspace.

▪ Can interface any data historian.

▪ Cost of engine maintenance is shared

by multiple users.76

PHI* - Pump Health Indicator

Identify degradation WITHOUT:

▪ Without flowrate

▪ Use commonly available real-time data

Enables:

▪ Monitor Pump Health in Real Time

▪ Calibrate Flowrate Models where well tests is not available fro calibration

77

Compares actual measured differential head

to theoretical factory curve head at a given

flowrate…usually the production test rate.

This method requires knowledge of:

1) Rates

2) Fluid ➔ SG

3) PVT

Traditional ESP Diagnostic & Health Monitoring

Not Available

without a well

test

PiPdPPW −=

FluidPHeadDiff /. =

Discharge

Pressure

Intake

Pressure

78

The Alternative Reference Curve– Typically this is the factory water test curve

– But can be an in-situ curve based on flowrate measurement

Conventional WITH

Flowrate

NEW WITHOUT

Flowrate

𝐷𝑃𝑟𝑃𝑟

= 𝑓 𝐷𝑃

DP

/ P

ow

er (

psi

/hp

)

DP

= P

um

p D

iffe

ren

tial

Pre

ssu

re (

psi

)

Flowrate (bpd) DP = Pump Differential Pressure (psi)

• SG independent

• Power dependent

• SG dependent

• Power independent

79

Examples of Flowrate Independent Characteristic Curves

80

PHI=1.0

Calibration

Mode #2: For a few weeks following ESP

start-up:

• Pump is new ➔ no wear

• Based on PVT ➔ No Viscosity

degradation

• High intake pressure, GOR ~ Rs ➔ no

gas degradation

Mode #1: Provides identification of

change in pump performance change

independently of flowrate due to:

• Wear

• Gas Degradation

• Viscosity Degradation

PHI has two modes

PHI =

𝑫𝑷

𝑷 𝒂𝒄𝒕𝒖𝒂𝒍𝑫𝑷

𝑷 𝒓𝒆𝒇𝒆𝒓𝒆𝒏𝒄𝒆

=𝑪𝜼

𝑪𝒒

Cη = ηa / ηr Efficiency Degradation

Cq = Qa / Qr Flow Degradation

DP = Pd-Pi Pump Differential Pressure

P Pump Absorbed Power

81

PHI Example, SPE paper 176780

PHI

PH

I =

𝑫𝑷 𝑷

𝒂𝒄𝒕𝒖𝒂𝒍

𝑫𝑷 𝑷

𝒓𝒆𝒇𝒆𝒓𝒆𝒏𝒄𝒆

=𝑪𝜼

𝑪𝒒

PHI over 16 months PHI zoom-in on last 5 days

Insulation

Degradation

predicting Motor Burn

PHI = 1.0 ➔ good condition

82

Calibration Achieves Rate Accuracy < 2.5%

Courtesy of SPE-183337- Testing the Untestable... Delivering Flowrate Measurements with High

Accuracy on a Remote ESP Well

PHI =

𝑫𝑷

𝑷 𝒂𝒄𝒕𝒖𝒂𝒍𝑫𝑷

𝑷 𝒓𝒆𝒇𝒆𝒓𝒆𝒏𝒄𝒆

=𝑪𝜼

𝑪𝒒

Cη = ηa / ηr Efficiency Degradation

Cq = Qa / Qr Flow Degradation

DP = Pd-Pi Pump Differential Pressure

P Pump Absorbed Power

PHI = = 1.0 ➔ Liquid Rate model calibrated

Calibrated downhole liquid rate

Well test results

DP

/ P

ow

er (

psi

/hp

)

DP = Pump Differential Pressure (psi)

Actual matched to

Reference (pump

curve) to achieve

PHI=1.0

▪ Following ESP start-up initial calibration based on PHI (Pump Health Indicator) =1.0 as➢ Pump is new ➔ no wear➢ Based on PVT ➔ No Viscosity or gas degradation

▪ If PHI remains constant at 1.0 ➔calibration is maintained.

▪ Verified against well tests & accuracy achieved• SPE 183337 Accuracy <2.5%• SPE 2471526: Accuracy < 1%

83

PHI =1.0, liquid rate & WC are

calibrated

PHI Detecting Wear(SPE 185144)

Rise in PHI detects pump mechanical wear after prolonged operation in downthrust in sandy environment

Pump Operating in downthrust in sandy environment cause of wear

Intake pressure rising with drop in production

Liquid rate trend is correct despite losing calibration (rise in PHI)

PHI = 1.0 ➔ good condition

84