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11/18/2008
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SPE Distinguished Lecturer Program
The SPE Distinguished Lecturer Program is funded principally through a grant from the SPE Foundation.
The society gratefully acknowledges the companies that support this program by allowing their professionals to participate as lecturers.
Special thanks to the American Institute of Mining, Metallurgical, and Petroleum g gEngineers (AIME) for its contribution to the program.
Society of Petroleum Engineers Distinguished Lecturer Programwww.spe.org/dl
Pitfalls to Avoid in AssessingA ifi i l Lif R Lif P fArtificial Lift Run-Life Performance
Francisco AlhanatiC-FER Technologies
Society of Petroleum Engineers Distinguished Lecturer Programwww.spe.org/dl
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Impact on Economics
• Artificial Lift Run Life Performance• Artificial Lift Run-Life Performance directly affects: – Work over frequency– Work over costs– Production losses
Impact of ESP Run-Life
20 Wellsaverage oil production per well: 1,000 bpdaverage intervention cost: 150 k US$average workover & waiting time: 60 daysoil price: US$60/bbl
Overall Workover CostsOverall Workover Costs
$100
$150
$200
$250
llion
s / y
ear
15%
20%
25%
30%
% re
venu
e
$0
$50
$100
0 120 240 360 480 600 720 840 960
Average Runtime (days)
mil
0%
5%
10%
%
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AL Run-Life Performance is important
• Key Performance Indicator (KPI)• Key Performance Indicator (KPI) – effects of changes in operational
conditions, equipment selection and operational practices
– used in many alliance contracts between operators and vendorsoperators and vendors
Assessing AL RL Performance
• Not as simple as it may sound• Not as simple as it may sound– Several measures used throughout the
industry– Trends are often misleading
• Issues must be understood, so that– Pitfalls can be avoided– Proper RL measures can be selected
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• For many installations Run Life is not
Run-Life and Runtime
• For many installations, Run-Life is not known, only Runtime– Systems that are still running– Systems that were pulled for other reasons
than system failure
Censoring
• The data is said to be “censored”• The data is said to be censored• One can only hope to obtain estimates
of average Run-Life• Based on all the systems Runtime
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Run-Life Estimates
• Average Runtime can be calculated for: – All systems (pulled or still running)y (p g)– Running systems only – Pulled systems only – Pulled and Failed systems only
• All these averages can be calculated based on different exposure timesp– Time-in-Hole, Total Runtime, Actual Runtime
• Over different (calendar) periods of time– Last two years, last five years, etc.
Run-Life Estimates• Average Runtime of pulled systems:
• Includes failure of other “systems”: tubing, sand control etcsand control, etc.
• It is a reasonable indicator of the overall production system reliability
• But not of the AL system reliability
• Average Runtime of failed systems:g y• Also affected by failures of other “systems”• Not a good indicator of the AL system
reliability either
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Run-Life Estimates
• At a certain point of time, all you can have is a statistical “best estimate” orhave is a statistical best estimate , or “expected value” of average Run-Life or Mean Time to Failure (MTTF)
Run-Life Estimates• Average Failure Rate:
– Number of failures per well over a period of time
• MTTF estimate:– the inverse of the average failure rate– ratio of the total time in operation (for all
systems pulled or still running) to thesystems, pulled or still running) to the number of failures:
failed
TTMTTF runningpulled
#∑ ∑+
=
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What is a Failure?
• Failure:Failure:– The termination of the ability
of an item to perform its required functions
ISO 14224: Petroleum and Natural Gas Industries: Collection and Exchange of Reliability and Maintenance Data for Equipment
Common Pitfalls
• Early Failures versus Frequent Failures• Early Failures versus Frequent Failures • Improvement versus Aging• Component Reliability and System RL • Failure Mechanism versus Failure
CauseCause
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ESP-RIFTS DataLocations of Fields
BP Nexen Shell
Chevron PDVSA Shell PDO
ConocoPhillips Petrobras TNK-BP
EnCana Repsol YPF TOTAL
ExxonMobil RosneftKuwait Oil Company Saudi Aramco
ESP-RIFTS: ESP Reliability and Failure Tracking System
Common Pitfalls
• Early Failures versus Frequent Failures• Early Failures versus Frequent Failures• Improvement versus Aging• Component Reliability and System RL • Failure Mechanism versus Failure
CauseCause
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What is the least reliable component?Is it the gas
Average Runtime of Failed
200
300
400500
600
700800
900
erag
e R
un T
ime
(day
s)
Is it the gas separator?
0
100
Cable DownholeSensors
GasSeparator
Motor Pump PumpIntake
Seal
ESP Component
Ave
Failure Rate
300
350
400
6 / d
ay)Which is more
li bl ?
0
50
100
150
200
250
ESP Cable DownholeSensors
GasSeparator
Motor Pump PumpIntake
Seal
ESP Component
Failu
re R
ate
(x10
-6reliable?The motor or the cable?
Common Pitfalls
• Early Failures versus Frequent Failures• Early Failures versus Frequent Failures • Improvement versus Aging• Component Reliability and System RL • Failure Mechanism versus Failure
CauseCause
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Is the system reliability improving?Or are the systems just aging?
4 0 MTTF (3 Wi d )
1 5
2.0
2.5
3.0
3.5
4.0
Run
-Life
Est
imat
e
MTTF (3 yr Window)
Average Runtime of Running
0.0
0.5
1.0
1.5
1997 1998 1999 2000 2001 2002 2003
Calendar Year
R
Common Pitfalls
• Early Failures versus Frequent Failures• Early Failures versus Frequent Failures • Improvement versus Aging• Component Reliability and System RL• Failure Mechanism versus Failure
CauseCause
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Is the equipment from both
manufacturers
Survival Curve
20
30
40
50
60
70
80
90
100
S (t
)Manufacturer AManufacturer B
equally reliable?
Failure Rate
300
350
400
6 / d
ay) Manufacturer A
Manufacturer B
0
10
20
0 12 24 36 48 60 72 84 96
time in operation (months)
0
50
100
150
200
250
Cable Gas Separator Motor Pump Pump Intake Seal
ESP Component
Failu
re R
ate
(x10
-6
Common Pitfalls
• Early Failures versus Frequent Failures• Early Failures versus Frequent Failures • Improvement versus Aging• Component Reliability and System RL • Failure Mechanism versus Failure
CauseCause
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Failure Classifications
• Reason for Pull – Suspected system failure or any other reason– Suspected system failure or any other reason – e.g.: stimulation, re-completion
• Primary Failed Item and Descriptor– Component (or part) in which the failure likely
initiated, and likely mechanism – Based on observations during pull or teardown– e.g. motor burn g
• Failure Cause:– The circumstances during design, manufacture or
use which have led to a failure– e.g. improper assembly during installation
Failure Analysis Process
System Failure Reason for Pull defined:
System Pull and Teardown- Items and Descriptors defined:
e.g., Shorted MLE
- Reason for Pull defined:e.g., No flow to surface
Failure Investigation- Cause defined:e.g., Installation; Improper Assembly
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Do I have a manufacturing (QC) problem?Or do I have an operational problem?
Number of Failures by Failure Cause
40
60
80
100
120
mbe
r of F
ailu
res
Completion
Installation
Manufacturing
Normal or ExpectedWear-and-TearOperation
Other
0
20
Cable GasSeparator
Motor Pump PumpIntake
Seal
ESP Component
Num
Other
Storage andTransportationSystem Design /Selection
Conclusions
• There are several measures used throughout the industrythroughout the industry
• One needs to understand their meaning to properly interpret the trends
• Best picture of the situation likely requires looking at several measuresI t i th h• Improvement requires thorough investigation of the failure causes
• Be aware of the pitfalls !
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Acknowledgement
• ESP-RIFTS JIP Participants:ESP RIFTS JIP Participants:– BP - Petrobras– Chevron - Repsol-YPF– ConocoPhillips - Rosneft– EnCana - Shell
ExxonMobil StatoilHydro– ExxonMobil - StatoilHydro– KOC - TNK-BP– Nexen - Total