11/18/2008
SPE Distinguished Lecturer Program The SPE Distinguished Lecturer Program is funded principally through a grant from the SPE Foundation. T...
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 g Metallurgical, g and Petroleum Mining, Engineers (AIME) for its contribution to the program. Society of Petroleum Engineers Distinguished Lecturer Program www.spe.org/dl
Pitfalls to Avoid in Assessing A ifi i l Lift Artificial Lif Run-Life R Lif Performance P f Francisco Alhanati C-FER Technologies
Society of Petroleum Engineers Distinguished Lecturer Program www.spe.org/dl
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Impact on Economics • Artificial Lift Run Run-Life Life Performance directly affects: – Work over frequency – Work over costs – Production losses
Impact of ESP Run-Life
20 Wells average oil production per well: 1,000 bpd average intervention cost: 150 k US$ average workover & waiting time: 60 days oil price: US$60/bbl
Overall Workover Costs $250
30% 25% 20%
$150
15% $100
% revenue
milllions / year
$200
10% $50
5% 0%
$0 0
120
240
360
480
600
720
840
960
Average Runtime (days)
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AL Run-Life Performance is important • Key Performance Indicator (KPI) – effects of changes in operational conditions, equipment selection and operational practices – used in many alliance contracts between operators and vendors
Assessing AL RL Performance • 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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Run-Life and Runtime • For many installations, installations Run-Life 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” 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 y (pulled or still running) (p g) Running systems only Pulled systems only Pulled and Failed systems only
• All these averages can be calculated based on p times different exposure – 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 control, etc etc. • It is a reasonable indicator of the overall production system reliability • But not of the AL system reliability
• Average g Runtime of failed systems: 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 best estimate” 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 the systems, number of failures: MTTF =
∑T
pulled
+ ∑ T running
# failed
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What is a 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 Improvement versus Aging Component Reliability and System RL Failure Mechanism versus Failure Cause
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ESP-RIFTS Data Locations of Fields
BP
Nexen
Shell
Chevron
PDVSA
Shell PDO
ConocoPhillips
Petrobras
TNK-BP
EnCana
Repsol YPF
TOTAL
ExxonMobil
Rosneft
Kuwait Oil Company
Saudi Aramco
ESP-RIFTS: ESP Reliability and Failure Tracking System
Common Pitfalls • • • •
Early Failures versus Frequent Failures Improvement versus Aging Component Reliability and System RL Failure Mechanism versus Failure Cause
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Average Runtime of Failed Ave erage Run Time (days)
What is the least reliable component? Is it the gas separator?
900 800 700 600 500 400 300 200 100 0 Cable
Downhole Sensors
Gas Separator
Motor
Pump
Pump Intake
Seal
Pump Intake
Seal
ESP Component
Which is more reliable? li bl ? The motor or the cable?
Early Failures versus Frequent Failures Improvement versus Aging Component Reliability and System RL Failure Mechanism versus Failure Cause
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Is the system reliability improving? Or are the systems just aging? 40 4.0
MTTF (3 yr Window) Wi d )
3.5
Average Runtime of Running
Run-Life Estimate R
3.0
2.5
2.0
15 1.5
1.0
0.5
0.0 1997
1998
1999
2000
2001
2002
2003
Calendar Year
Common Pitfalls • • • •
Early Failures versus Frequent Failures Improvement versus Aging Component Reliability and System RL Failure Mechanism versus Failure Cause
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Survival Curve
Is the equipment from both manufacturers equally reliable?
100 90 80
Manufacturer A Manufacturer B
70
50 40 30 20 10 0 0
12
24
36
48
60
72
84
96
time in operation (months)
Failure Rate 400
Failure Rate (x10-66 / day)
S (t)
60
350
Manufacturer A
300
Manufacturer B
250 200 150 100 50 0 Cable
Gas Separator
Motor
Pump
Pump Intake
Seal
ESP Component
Common Pitfalls • • • •
Early Failures versus Frequent Failures Improvement versus Aging Component Reliability and System RL Failure Mechanism versus Failure Cause
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Failure Classifications • Reason for Pull – 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. g motor burn
• 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: e.g., No flow to surface
System Pull and Teardown - Items and Descriptors defined: e.g., Shorted MLE
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?
Num mber of Failures
Number of Failures by Failure Cause 120
Completion
100
Installation Manufacturing
80
Normal or Expected Wear-and-Tear
60
Operation
40
Other
20
Storage and Transportation System Design / Selection
0 Cable
Gas Separator
Motor
Pump
Pump Intake
Seal
ESP Component
Conclusions • There are several measures used throughout the industry • One needs to understand their meaning to properly interpret the trends • Best picture of the situation likely requires looking at several measures • Improvement I t requires i thorough th h investigation of the failure causes • Be aware of the pitfalls !
