Alyeska%20Experience%20with%20Data%20Integration%20on%20Trans%20Alaska%20Pipeline

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Experience with Data Integration on theTrans
Alaska Pipeline

• Alyeska Experience with Data Integration on Trans
  Alaska Pipeline

2
TAPS Background

• 800 Mile - Crude Oil Transmission Pipeline
• North Slope to Valdez Alaska
• 48 Dia. 0.5 wall thickness
• Three Construction Modes to Consider
• 376 Miles Conventional Below Ground pipeline
• 420 Miles Above Ground to avoid unstable
  permafrost
• 4 Miles Insulated/Refrigerated Buried
• System Startup - August 1977
• 24 Yrs of Operation
• 2.1 MMBPD Capacity, 1.0 MMBPD Throughput
• Over 13 B bbls transported

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TAPS ILI Background

• TAPS - History of In Line Inspection (ILI)
• Annual Inspections Since 1979
• 58 Smart Pig Runs over operating life
• Both UT and MFL wall loss pigs used - UT is now
  primary tool
• Curvature/Deformation Pigs used
• Predominate Operating Risks Addressed by ILI
• Corrosion
• Settlement/Curvature
• Deformation/Third Party damage -
• During construction and operation

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Experience with Data Integration

• Data Integration Depends on Decisions Required
• Focus on Decision Support not just Data
  Management
• A Management System needed - to manage changes
• Decisions Based on Risk - Contain Uncertainty
• Decisions depend on defect type
• Corrosion, Dents, Curvature, Interaction of
  defects
• Decisions depend on pipeline location and data
  limitations
• High Risk v Normal Risk Locations
• Intervention Criteria Based on Risk
• Interaction with ILI Vendors a Must
• Pig Data Can Be Used to Assess Cathodic Protection

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Data Integration Description

• Oracle Data Base - Intranet Application- By Pipe
  Joint (Contains 105,000)
• Pipe Data - Grade, Thickness, MAOP, Hydrotest
  data, Bend data, Mode
• Pipe Features - Insulation, Casing, Coating,
  River Weights, etc,
• Hydraulics Data - Functional MOP
• Pig Data - Corrosion/Curvature/Deformation -
  Contains Graded, Not Raw Data,
• CP Data - CIS, CP Coupon Data
• Embeds Routine Queries and Decisions
• Corrosion Defect Evaluations, RSTRENG
• Outputs Routine Reports
• Ranking by penetration, bursting pressure, SF,
  Years to Dig, Etc.
• Integrated Data Displays - GIS not mandatory
• Contains Information needed for Maintenance
  Decisions

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Management System Elements

• A management system is needed to manage change
• Alyeska Integrity Management System (AIMS) has 5
  elements
• Scope Objectives defined
• Secure, readily accessible environment
• Allows Accurate and efficient maintenance
  decisions
• Distributes data in a single source
• Maintains a record of decisions made
• Procedures written
• Data Collection, Quality Assurance, Security etc.

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Mgmt System Elements (Cont)

• Accountable Resources - Roles Defined
• Data Base Developer
• Data Base Administrator
• Data Owners, Data Entry
• System Users
• IT Maintenance Support
• Data Management Performance Measures established
  Based on Objectives
• Feedback Processes established
• AIMS Assessment - Improvement of Decision Support
  Management Plan
• Risk Assessment - Assess risks e.g. bad
  algorithm, data corruption, human error
• Technology Assessment - Take advantage of new
  technologies, i.e. GIS
• Compliance Assessment - Adapt to new regulations,
  new industry standards
• Business Assessment - Strategic planning and
  budgeting

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Defect Types and Failure Modes

• Corrosion
• Penetration
• Bursting
• Dents
• Outside Force/Third Party Damage - Top Half of
  Pipe
• Bottom Half of Pipe -
• Dents w Stress Riser (Metal Loss or Contact with
  Weld)
• Curvature and Curvature w Corrosion
• Straight Pipe
• Field Bends

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Risk Classification Sets Urgency

• Decisions depend on Risk
• Consequences depend on pipeline location
• Data Limitations increase risks (pig data, CP
  data)
• High Risk Areas - 6 different types
• DOT High Consequence Areas 40 miles
• Locations where leak Could Affect
• Commercially Navigable Waterways
• High or Other Population Areas
• Unusually Sensitive Areas - Endangered Species
• Other (Discretionary) High Consequence Areas 20
  miles
• Locations Within
• Major Streams or Floodplains
• Inaccessible Areas

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Action Urgency (Continued)

• High Risk Areas (Continued)
• Areas where Pig Performance is Limited 15 miles
• Near Welds, 3D Bends, Slack Line, Wax affected
  locations
• Active Corrosion Areas - 9 miles
• Statistically Active Corrosion, High projected
  corrosion
• Areas with Limited Cathodic Protection - 5 miles
• Under Insulation, Shorted Casings, Known
  Disbonded Coatings
• Notably Corroded Areas - 1 mile
• High Probability of Exceedance, Potential for
  NAC, SCC, MIC
• Normal Risk Areas - Other areas not designated
  High Risk
• 90/800 miles (11) designated high risk

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Intervention Criterion (Triage)

• No Action - Required
• Intervene - Corrective Maintenance within Year
  of Discovery1 or as required by regulation
• Expose pipe, evaluate defect, repair if necessary
• lower operating pressure
• Investigate - Predictive Maintenance within 3
  Years
• Rank Severity based on all available data
• Expose pipe, evaluate and repair in order of
  Severity Rank
• Note1
• Discovery Means - Data is available, of
  sufficient reliability, for an operator to
  clearly determine that intervention is required.

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Intervention Criterion (Cont)
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Decision Process
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Interaction Needs with ILI Vendors

• Raw data interpretation by Operator needed
• Pig Performance assessment
• Routine Coordination w Vendor
• Comparison between Field and Pig data

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CP Mitigation Decisions

• Pig Data Can Be Used for more than dig decisions
• Statistically Active Corrosion -
• Based on 100 foot Moving Average Pig Call Depth
• Identifies Statistically Active Corrosion (3-10
  mpy vs 0-3 mpy)
• May indicate need for CP Mitigation in spite of
  Good CP Data
• Projected Pig Features -
• Years to Dig Projected to determine number of
  corrosion investigations likely in future.
• Economic Model Compares Cost of Corrosion
  Investigations vs. Alternative Maintenance such
  as Additional CP or Coating Refurbishment.
• Corrosion Data Overlays - GIS like display
• Used to Display Relevant Corrosion Data in one
  source
• Supports corrosion decision making and planning

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Corrosion Data Overlays
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Summary - Lessons Learned

• Data Integration Depends on Decisions Required
• Focus on Decision Support not just Data
  Management
• A Management System needed - to manage changes
• Decisions Based on Risk - Contain Uncertainty
• Decisions depend on defect type
• Corrosion, Dents, Curvature, Interaction of
  defects
• Decisions depend on pipeline location and data
  limitations
• High Risk v Normal Risk Locations
• Intervention Criteria Based on Risk
• Interaction with ILI Vendors a Must
• Pig Data Can Be Used to Assess Cathodic Protection

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