4 Ways to Get More from Your RBI Program.pdf

4 Ways to Get More from Your RBI Program
W E B I N A R I S S T A R T I N G S O O N

Lynne Kaley
VP, Research & Development
Ryan Myers
Product Manager
4 Ways to Get More from Your RBI Program

Agenda
Property of Pinnacle
- Get more value from RBI today by:
1. Improving the probability of failure
calculation to increase accuracy.
2. Better leveraging subject matter expertise.
3. Quantifying the value of inspections
4. Improving inspection planning and
recommendations
- Takeaways and Questions
Overview

Problem Setup
• RBI has been widely accepted
and used for 10-15 years
• Initial value of RBI has been
realized
• Still experiencing failures
• Owner-operators are looking for
ways to further improve
programs
Current Opportunities
• Improve accuracy for thinning
evaluation by effectively using CML
data to make better decisions
• Balance and focus high demand
resources
• Quantify inspection history and
recommendations
• Optimize availability, risk and cost

Mechanical
Integrity
Data
Science
Optimization

• Probabilistic evaluation called
Lifetime Variability Curve (LVC)
• Uses a combination of first
principles engineering and data
science
• Creates POF and risk projection
models for all asset types, not as
dependent on conservative
assumptions
- Data-based objectivity
- Quantification of statistical
uncertainty
Improved Thinning POF
Opportunity #1

Evaluating Thinning Risk
à
Heavily
SME-based
Heavily Inspection
Results-based
Opportunity #1

API 581 vs. LVC - No Inspection
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Probability
of
Failure
Probability of Failure with Time
API
LVC
Opportunity #1

API 581 vs. LVC – with Inspection
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Probability
of
Failure
API
LVC
Opportunity #1

API 581 vs. LVC – with Inspection
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Probability
of
Failure
LVC
API
Opportunity #1

Recommendation
Replace API 581 Thinning POF
Model with a Lifetime
Variability Curve (LVC)
Recommendation and
Benefits - Improved Thinning POF
Benefits
• Improves Thinning POF calculation accuracy and precision
• Uses all actual inspection data available and inspection
results findings
• Blends both SME assigned degradation rate and inspection
results
• Quantifies uncertainty of inspection data and degradation
rates
• Dynamically updates projections, trends, and uncertainty
with more information based on how data behaves
• POF modeled using a critical thickness value where actual
failure is expected to occur, removes unnecessary
conservatism
Opportunity #1

• Focus SME on highest value-added activities
- Qualified resources are in high demand
- Demands involve several programs (IOWs, DMRs, RBI)
• Balance supporting competing programs (DMR reviews, IOWs, PHAs, RBI)
requires most efficient use of SME time
• Build SME confidence in inspection data
- Provide information for SME in damage assignments
- SME guidance to identify or confirm areas of concern
- Improve assessments and results by incorporating data analytics
Better Leverage SME Resources
Opportunity #2

Heat Exchanger Outlet Example
• SME identified two families within a
corrosion loop:
− Horizontal Condensate Locations (blue)
− Deadleg (green)
• Data science confirmed potential
accelerated corrosion on
horizontal condensate locations
• Data science found no evidence of
accelerated corrosion or serious
issues in deadleg CML
Opportunity #2

Furnace Outlet Example
• SME identified three families
across four corrosion loops:
− 1st elbows after furnace outlet
(purple)
− 2nd- & 3rd elbows after furnace
outlet (orange)
• Data Science identified a 3rd
family from data:
− Vertical risers (red)
− Confirmed by SME
Opportunity #2

• Cross-validation with MI, Materials and Corrosion, and Data Science
• Review Major Discrepancies Between Data Analytics and SME Expectation
- Identifies potential problems based on data or major gaps
- Quantifies rates and uncertainty in data rather than using assigned rates with
some level of conservativeness considered
- Identifies areas of concern not anticipated
• Improved Asset Strategies
- Inspection
- Repair/Replace
- Upgrades
- Integrity Operating Windows
Multi-Disciplinary Risk Review
Opportunity #2

Benefits
• Data science provides data cleansing, comparisons,
qualification, and interpretation
• Data science identifies overlooked or outside of the SME
experience
• Inform and support SME assigned mechanisms and rates
• Better correlation between SME assigned rates and
inspection history
• Optimizes use of scarce SME resource time and focuses
their efforts
- Understanding “Why” a problem is occurs
- Identifying possible solutions to problems
Recommendation and
Benefits –Better Leverage SME Resources
Recommendation
Incorporate data science in
multi-disciplinary reviews
Opportunity #2

• Quantitative, objective method desired to:
- Assign inspection credit and recommended specific inspection
- Quantify uncertainty
• Quantitative Task Effectiveness (QTE) quantifies economic value of
every, inspection, maintenance, repair, or replacement activity
• Provides specific inspection guidance and expected results to be used
during implementation of the inspection plan
• Coverage is adjusted based on actual findings so that uncertainty is
minimized
• Determines the inspection coverage required and the uncertainty
associated with inspection
Quantify Inspection Value
Opportunity #3

• Quantitative Task Effectiveness
(QTE) provides extent of
inspection based on desired
level of confidence
• Incorporates probability of
detection, % susceptible area
coverage, and actual
inspection history
Methodology
Opportunity #3

• Damage Mechanism: Unspecified Internal Corrosion (UIC)
• 27 CMLs total – but we only need 7 for 95% confidence (cumulative)
General Degradation Example
Historical Readings
Projected Thickness
Per CML In 2025
CML Selection
Opportunity #3

• Thickness data used to predict minimum thickness of the uninspected
areas
• Required inspection increases as data variability increases
Local Degradation and
Extreme Value Analysis (EVA)
Low Data Variance
Predictions close data
High Data Variance
Predictions more extreme
Thickness, inch
Thickness, inch
Normalized
Failures
Normalized
Failures
Opportunity #3

• If data supports indication of localized degradation, EVA is valid
• If data does not support indication of localized damage due to poor
CML placement or coverage, use a Bayesian EVA approach
- SME to support improved CML placement
• With more data from inspection, SME expectation is either reinforced or
denied
Local Degradation Analysis
Standard EVA
Overly optimistic
Bayesian EVA
Uses SME knowledge
to make accurate
predictions
Opportunity #3

• Heat Exchanger Tube Bundle:
- 216 Tubes
- Low Susceptibility to Cracking
- No Cracking Inspections
- Probability of Detection: 90%
• Use API 581 POF methodology and cracking
inspection history to determine the extent
of inspection required for desired
confidence level
- Statistical analysis with optional Bayesian
incorporation of SME expectation
• For 90% confidence, a total of 81/216 tubes
should be inspected
Cracking
A total of 81/216 tubes should
be inspected for a Low
susceptibility to cracking
Opportunity #3

• Total Risk reaches the Risk Target in 9/2022 - Driven by Thinning and
cracking as a secondary failure mode
Results Example
0
20,000
40,000
60,000
80,000
100,000
120,000
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Risk
Total Risk
Thinning Risk
Cracking Risk
RiskThreshold
Total No Inpection
Thinning No Inspection
Opportunity #3

• Pareto Principle on an Asset
• 350 CMLs
• 20% of CMLs are responsible
for 80% of the total risk
• Diminishing returns with
inspection beyond a certain
point
Risk with Inspection
Opportunity #3

Recommendation
Combine data science
techniques with SME
knowledge to quantify
historical inspections and
provide more prescriptive
inspection recommendations
Recommendation and
Benefits – Quantify Inspection Value
Opportunity #3
Benefits
• Eliminate subjective assumptions and rules of thumb
• Specific inspection plans includes e.g. # tubes, # of CMLs,
area of coverage)
• Inspection recommendations based on expected and
actual results
- If damage is less severe than expected, inspection
confidence is increased
- If damage is more severe than expected, inspection can be
increased during inspection or confidence is decreased
- Accept lower level of confidence in asset condition
• Enables focus of limited resources where they add the
most value

• Current industry approach to inspection
planning uses HSE risk modeling over time
- Identifies date assets requiring inspection to
achieve/maintain a target risk level
• Creates value using task optimization
- Balances the impact of HSE risk, system
availability and cost of the activities
- Quantifies a benefit to cost (availability increase
or risk reduction for a specific investment)
• Compares current practices as a baseline to
an optimized plan
- Project activities 10+ year ahead to optimize
activities
Optimize Inspection Planning
and Recommendations
Cost
HSE
Risk
Availability
Task
Optimization
Parameters
Opportunity #4

• Dynamic cause and effect link
between data points, assets and
the facility
• Model how every activity
impacts facility performance
Forecasting System Availability
Opportunity #4

• Maximize reliability return-on-
investment (ROI), reaching the ideal
balance across conflicting metrics of
availability, risk, and cost.
• By connecting asset data, predicted
failure dates, field execution
constraints, and system availability
we can goal-seek reliability
Reliability & Performance Optimization
Opportunity #4

Task Optimization Summary
Opportunity #4

Task Optimization Summary
• 10 Years Simulated
• Availability Excludes TAR
QRO Analysis Conducted
Baseline Change Task Optimization
Availability Availability
98.9% 0.9% 99.8%
Forecasted Maintenance Cost Forecasted Maintenance Cost
$514K $162K $352K
• Unit Margin: Assumed $1M/Day
• Task Optimization Only – No upgrades
Opportunity #4

Recommendation
Use a balanced approach
considering Availability,
Cost, and HSE Risk
Recommendation and Benefits –
Optimize Inspection Planning and Recommendations
Opportunity #4
Benefits
• Improve inspection recommendations with a task
optimization:
- Increase inspection coverage and reduce inspection intervals to better
mitigate high risk areas
- Decrease inspection coverage and increase inspection intervals to
reduce low value spending of limited resources
• Understand and quantify alternative strategies to
inspection through simulation:
- Repair/Replace in Kind
- Metallurgy Upgrade
- Addition of Corrosion Inhibitor
- Implementation of Integrity Operating Windows and Process Control

• RBI is widely used, there are opportunities for updating/upgrading the current
technology using newer, data-driven approaches
• Better use of SMEs combined with data science to improve risk management
and inspection practices
• Opportunity to remove subjectivity of inspection grading and generate more
specific inspection recommendations
• Broaden optimization of inspection to include:
- Balance system availability and cost in addition to HSE risk
- Benefit to cost analysis for improved prioritization and planning
Key Takeaways

Questions?

4 Ways to Get More from Your RBI Program.pdf

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