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Alyeska Experience with Data Integration on Trans Alaska Pipeline

Experience with Data Integration on theTrans Alaska Pipeline

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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)

High Risk Normal RiskDefect Type Investigate Intervene Investigate Intervene

Corrosion/Metal LossPenetration RWT < 65 % NWT RWT < 60% NWT RWT < 55 % NWT RWT < 50% NWT

Bursting YTD <= M*Inspct Intvl PRSTREN < 95 % MOP YTD <= 1*Inspct Intvl PRSTREN < 100 % MOP

Dent/OvalityTop Half Def > 0.75 in (1.5% Dia) Fatigue Unacceptable Def > 0.96 in (2% Dia) Fatigue Unacceptable

Bottom Half Def > 2.88 in (6% Dia) Fatigue Unacceptable Def > 2.88 in (6% Dia) Fatigue Unacceptable

Dent w Stress Riser Any Dent w Metal Loss If no mitigating factors Any Dent w Metal Loss If no mitigating factors

CurvatureStraight Pipe Curv > 70 % Kcr Wave Form > .25 in Curv > 85 % Kcr Wave Form > .25 in

Delta Curv > 10 % Delta Curv > 20 % Delta Curv > 10 % Delta Curv > 20 %

Field Bends Delta Curv > 10 % Delta Curv > 20 % Delta Curv > 10 % Delta Curv > 20 %

Wave Form > .25 in Wave Form > .25 in

Curvature w Metal LossStraight Pipe Curv > 70 % Kcr SAFE Unacceptable Curv > 85 % Kcr SAFE Unacceptable

Delta Curv > 10 % Delta Curv > 10 %

RWT < 65 % NWT RWT < 55 % NWT

YTD <= 2*Inspct Intvl YTD <= 1*Inspct Intvl

Field Bends Delta Curv > 10 % SAFE Unacceptable Delta Curv > 10 % SAFE Unacceptable

RWT < 65 % NWT RWT < 55 % NWT

YTD <= M*Inspct Intvl YTD <= 1*Inspct Intvl

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Decision ProcessDecision Criteria High Risk Areas,

Defect Types,Intervention

Criterion, Min Digs

Pig Data

CDMCorrosion DataManagement

System

Start PigFeature

EvaluationProcess

FeatureExceeds

InvestigationCriterion

RecommendCorrective

Maintenance

FeatureExceeds

InterventionCriterion

PredictiveMaintenance

Ranking Process

IsRanking<

Minimum NoDigs

RecommendPredictive

Maintenance

ConductMaintenance

No ActionDocument Results

Update CDM

Corrective ActionDocument Results

Update CDM

Predictive ActionDocument Results

Update CDM

EndProcess

VendorInteraction

Criterion

Current Data

PreviousData

No

Yes

YesYes

No

YesYes

No ActionDocument Results

Update CDM No

EvaluateLessons Learned

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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

Pig Run Type Begin End Deep Length DEG RWT 85%M RSTRENG

1998 NKK UT 3168678.9 3168688.3 3168688.1 4.8 221 362 1004 984

1997 NKK UT 3168687.6 3168689.8 3168688.1 26.4 222 335 849 958

1996 NKK UT 3168687.5 3168693 3168688 66 226 362 869

1994 Pipetronic UT 3168687.6 3168693 3168688 64.8 211 382 905

1992 Pipetronic MAG 3168687.9 316868802 3168688 3.6 212 370 1025

Field Analysis 3168687.8 3168691.8 3168688.1 48 216 365 882 954

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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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