Transcript RAM Modelling in Projects - Asset Management Council

Asset Management Council – WA Chapter & Maintenance Engineering Society of Australia
Reliability Modelling for
Business Decisions
RAM Modelling in the Project
Design Phase
Friday 30th April, 2010
Paul Websdane
RAM Modelling for Business Decisions
• Project Design and Execute Phases
– Steps in Process.
– Examples & Learnings.
– Benefits.
• RAM in Operations phase
– Barriers & Benefits.
Introduction
– Snr Reliability Engineer – K2 Technology.
– Experience in Oil and Gas, Alumina, Mining,
Condition Monitoring, Pumping.
– RAM Tools & Packages;
• Many different packages are available.
• Each have strengths and weaknesses.
– Used RAM for analysis of large new projects,
small design changes, tank overhaul
scenarios, decisions on redundancy.
RAM Modelling Overview
• Tool to analyse and predict the availability /
reliability of an asset or facility.
• Reliability Block Diagrams (RBD) used.
• Use Equipment Capability & Reliability data.
• Maintenance Strategies & Schedules (optimise).
• Overall production impact - $$$$.
• Improved business decisions.
RAM models in the Design Phase
• Evaluate, Validate and Optimize design
– Availability & Reliability targets.
– Production capability.
– Bottlenecks & Big hitters – Critical Equipment.
– Redundancy levels.
– Sparing.
• Can “Design In” Reliability
– Focus improvement efforts early in design.
Model Basic Steps
• Understand system operating context, production
•
•
•
•
•
•
impact and cost of downtime.
Document assumptions.
Build the RBD and Reliability Data Register.
Populate with Reliability Data and details of
Maintenance Strategy / Shutdowns.
Analyse the System.
Update and refine over time.
Conduct Sensitivity analyses.
Reliability Block Diagrams
• Build Reliability Block Diagram from P&ID,
system drawings, PFDs;
– RBD’s represent the connections between
system components from a reliability
perspective.
– Does not show process flow.
Reliability Block Diagrams
2 x 100%
3 x 50%
RBD’s – Examples
Operating Context – what we need
• Design Capacity of each block.
• Redundancy.
• Impact on production
– No impact – why in the model?
• Single Point Vulnerabilities!
– Very important – do not miss these.
• Bypass capacity on failure
– Inbuilt work arounds that protect production.
Production Impact
Full Production
Each Turbine
32 kT/d
8 kT/d
For full production system requires 4
turbines online at all times (32kT/d)
Production Impact
Full Production
Each Pump
30 kT/d
15 kT/d
For full production system requires
2 pumps online at all times
(15kT/d)
Production Impact
• Bypass capacity – refines model with
actual production impact – also helps with
buy in from operations.
• Must understand the linkages between key
elements in the model.
Failure Modes / Reliability Data
• Understand dominant functional failures.
• Reliability data sourced from
– CMMS & Facility Operating History.
– Experienced operators.
– OREDA.
– Vendor.
Reliability Data
• CMMS
– Maintenance and failure history.
– Data accuracy? Job recording?
– How accurate is this across industry?
– Be careful – garbage in , garbage out.
• Facility Operating / Trip history
– Often stored outside CMMS.
– See your friendly Reliability Engineer.
Reliability Data
• Operators & Maintenance Resources
– Very valuable information resource.
– BUT – difficult to quantify losses without data.
– Useful information on Bypass capacity.
– Engage operations and maintenance where
possible.
Reliability Data
• Vendors and OREDA
– Some vendors have good history – check
operating context and environment.
– OREDA is of use – ensure a reasonable
population of equipment is available.
• Useful Reliability Data is available –
understand limitations and use with care.
Reliability Data Register
• Capture key data &
•
S-unit
references.
Hold workshop with
operations &
maintenance to validate
/ review data &
assumptions.
Description
Start date
Capacity (kT/d)
Air_CompA_NRB
Air Compressor A
1/01/2017
50
Air_CompB_NRB
Air Compressor B
1/01/2017
50
Dessicant_Air_DryerA_NRB
Dessicant Air Dryer A
1/01/2017
50
Dessicant_Air_DryerB_NRB
Dessicant Air Dryer B
1/01/2017
50
Inst_Air_Receiver_NRB
Instrument Air Receiver
1/01/2017
50
RELIABILITY DATA
Maintainable Component
MC
Failure Mode
Name
Failure Mode
Description
Bypass Capacity (%)
MTTF (years)
b
MTTR (hrs)
CV
Compressor
Critical_Failure
OREDA Critical Mean
0
3.2
1
22.8
1
Compressor_Motor
Critical_Failure
OREDA Critical Mean
0
2.12
1
47.5
1
Compressor
Critical_Failure
OREDA Critical Mean
0
3.2
1
22.8
1
Compressor_Motor
Critical_Failure
OREDA Critical Mean
0
2.12
1
47.5
1
Refrigerant_Air_Dryer
Critical_Failure
Spurious Trip
0
0.67
1
48
1
Refrigerant_Air_Dryer
Critical_Failure
Spurious Trip
0
0.67
1
48
1
Vessel
Critical_Failure
Significant External Leak
0
60
1
12
1
Maintenance
Analyse the Model Outputs
• Model outputs – typical.
RAM Model Results
Reliability %
(Unplanned Losses)
RAM Model Results
Availability %
(Unplanned + Planned Losses)
Reliability %
98.07
Availability %
96.38
Unreliability %
1.93
Unreliability %
3.62
Total Annual Downtime Equivalent (Days)
7.0
Total Annual Downtime Equivalent (Days)
13.2
Total Annual Downtime Equivalent (Hours)
168.9
Total Annual Downtime Equivalent (Hours)
317.1
Capacity
Level %
Days at
% Time at
Capacity Level per
Capacity Level
year
0%
0.63
2.31
0% - 33.3%
0.00
0.00
33.4% - 66.6%
0.2
0.68
66.7% - 99.9%
7.0
25.53
100%
92.19
336.48
Model Outputs - time
Facility XYZ Availability over time
100.00%
99.00%
98.00%
97.00%
96.00%
95.00%
94.00%
93.00%
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
Model Outputs
Facility - Relative Unreliability
1.83%
2.52%
3.52%
6.56%
System A
System B
System C
System D
System E
System F
System G
6.57%
12.01%
67.00%
Unit Interventions
Facility XYZ - Unit Interventions - Field Life
90.0
Corrective Interventions
Preventive Interventions
80.0
60.0
50.0
40.0
30.0
20.0
10.0
Glycol_Reboiler
Discharge_Scrubber
Heat Exchanger
Recirc_PumpB
Recirc_PumpA
Instrumentation
Glycol_Filter B
0.0
Glycol_Filter A
Number of Interventions
70.0
Unit Interventions
Corrective Interventions
Facility XYZ - Subsystem A - Unit Interventions per year
Preventive Interventions
35
30
Number of Interventions
25
20
15
10
5
0
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
Update and Refining the Model
• Assess Design Changes
– Latest updates.
– Quantify improvements .
– Incorporate maintenance (RCM).
• Shutdown analysis.
• Sensitivity Studies.
• Production Profiles.
Design Changes
• Add newer component (high reliability)
– System availability before – 98.0%
– System availability after - 99.2%
• Improvement of 1.2% or 4.4 days
production
@ $1million per day = $4.4m savings
Design Changes
• Redesign to save cost!
• Reduction in availability 0.5% or 1.8 days
production
@ $1million per day = $1.8m COST to
business.
• Can demonstrate impact of changes on
facility performance – better decisions are
made.
Sensitivity Studies
• Critical Equipment improvement options;
– water washing frequencies.
– more reliable equipment.
– maint strategy changes.
– Redundancy installed.
• Show me the money $$$$!
• Shutdown analysis – modify frequency
and durations – optimise.
Sensitivity Studies – Savings $$
Facilty XYZ Reliability Study
Impact of variations to design for Prod Water
and Recovered Oil Systems
Scenario Model
0
1
2
3
4
5
6
Base
1a
1b
2a
2b
3a
3b
Recovered
Oil Pump
Degasser
Water Pump
2 x 100%
2 x 100%
2 x 100%
2 x 100%
2 x 100%
2 x 100%
2 x 100%
2 x 100%
2 x 100%
2 x 100%
3 x 50%
3 x 50%
2 x 50%
2 x 50%
Produced
Water
Centrifuge
2 x 100%
2 x 100%
1 x 100%
2 x 100%
1 x 100%
2 x 100%
1 x 100%
Availability
98.00%
98.15%
97.81%
98.25%
97.89%
97.00%
96.25%
Comments
Base Case
New Assumptions added
Removed Centrifuge
Added 3rd Degasser Water Pump
Removed Centrifuge
Worst Case - 2 x 50% Degasser Water Pumps
Worst Case - 1 Centrifuge
Shutdown
Frequency
(wks)
Shutdown
Duration
(days)
Availability
Base Case - Current condition
Scenario 1 - short life, short
turnaround
25
10
94.29%
15
7
93.33%
-$
347,619
Scenario 2
30
11
94.76%
$
173,810
Scenario 3
Scenario 4 - long life, longer
turnaround
40
15
94.64%
$
130,357
60
25
94.05%
-$
86,905
Mud Thickener Descale
Scenarios
Savings ($)
per year
from Base Case
Sensitivity Studies – Savings $$
Sensitivity Study - Facility XYZ
from base case ($m)
Production Savings from base ($mill)
2.5
2
1.5
1
0.5
0
A
B
C
-0.5
-1
-1.5
-2
Option
D
E
Production Profiles - Refining
• Highlights system deficiencies over time.
Well Production Profile vs Capacity - XYZ
60
Well Production Profile
Max capacity
Production Rate (tpd)
50
40
30
20
10
0
2016
2018
• Applications
2020
2022
2024
2026
2028
2030
2032
2034
– well deterioration over time.
– Tank volume decrease (scaling) over time.
Improving Business Decisions
• Predict performance over time.
• Validate design changes.
• Quantify ($) cost and impact of failure.
• Identifies Critical Equipment
– where
– where
– where
– where
to
to
to
to
focus improvement efforts.
focus training.
consider redundancy.
hold critical spares (MTTR).
Asset’s Operations Phase
• Traditionally this is done poorly (if at all).
• Barriers
– Lack of buy in / support from operations &
maintenance .
– involve O&M in model build and assumptions.
– Modelling – inaccuracy, no understanding of
operating context.
– Rigorous review of data and facility
configuration – engage operations.
Asset’s Operations Phase
• Barriers
– Lack of confidence in model / data.
– Use valid data, document assumptions,
involve operations & maintenance.
• Review actual performance compared to
design over time – feedback into model.
• Consider the model to be “live” – regularly
update to improve accuracy.
Operations Phase - Benefits
• Highlight improvement opportunities.
• Justify cost of upgrades.
• Quantify BENEFITS of past projects.
• Assess effectiveness of maintenance.
• Assess risk of shutdowns – optimize
shutdown intervals.
Summary
• RAM modelling is a valuable tool in
Reliability Engineering.
• Important to use valid data and involve
operations & maintenance.
• Useful in all industries, for large and small
projects.
• Can improve business decisions by
quantifying loss and benefits in $ terms.
Questions?