Integrity Data Management and Integration

API BULLETIN 1178

FIRST EDITION, NOVEMBER 2017

Special Notes

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Foreword

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iii

Contents

Page

1. Scope 1

2. Normative References 1

3. Abbreviations 1

4. Benefits to an Enterprise Data Management System 3

5. Data Quality Oversight 4

   1. General 4

   2. Objectives 4

   3. Strategies and Policies 5

   4. Data Governance 5

   5. Data Quality Assessment 7

6. Transforming SME Knowledge into Data 7

7. Data Models 8

8. GPS Coordinates 8

   1. General 8

   2. Coordinate Nomenclature 9

   3. Datum Selection 9

   4. Accuracy 9

   5. Base Station Elevation 9

9. Alignment for the Purpose of Pipeline Integrity 10

   1. General 10

   2. Linear Referencing 10

   3. Weld Alignment 10

   4. Centerline 10

   5. Absolute Referencing 10

   6. Axial Position and Extent 11

   7. Circumferential Position 11

10. Sources of Measurement Error 11

    1. Spatial Error 11

    2. Severity Error 12

    3. Confidence Interval of the Error 12

11. Management of Change 12

    1. General 12

    2. New Data Relates to Old Data 12

    3. Accessing Historical Data 13

    4. Rerouting (Centerline Swap) 13

    5. Moving Fit Points 13

    6. Data Reconciliation 14

12. ILI Life Cycle 14

    1. General 14

    2. General Reporting Requirements 14

    3. Data Quality Letter and Preliminary Report 14

    4. Immediate Responses 15

    5. ILI Final Report Format 15

       v

       Contents

       Page

    6. Quality Assurance of Final Report before Acceptance 16

    7. Anomaly Assessment 16

    8. Excavation Program 19

    9. Provide Correlation Results to ILI Vendor 20

    10. Program Closeout and Establishment of Reassessment Intervals 20

13. Execution of Digs/Field Data Collection 21

    1. Pre-dig Information 21

    2. In-ditch Data Collection 24

14. As-built Asset Integration 30

    1. General Data Requirements 30

    2. GPS Survey 31

    3. Data Collection 31

    4. Virtual Pipeline Creation 32

    5. Data Storage 32

    6. Continuity of Linear Referencing Schema 33

    7. Baseline In-line Inspection 33

    8. Baseline Indirect Assessments 34

15. Over-the-Line Surveys (Indirect Assessments) 34

    1. General 34

    2. Alignment 34

16. Operational Data 35

17. Reporting and Data Mining 36

    1. General 36

    2. Data Integration/Analysis 36

    3. Data Integration and Analysis Output 37

    4. Integrity Metrics 37

Annex A (informative) Data Integration and Interpretation Report 38

Annex B (informative) Representative Data Listing 50

Bibliography 53

Figures

1. Schematic of Relevant Excavation Site Information 26

2. Examples of Common Feature Locations 35

Tables

1. Typical Location and Correlation Work Package Contents 23

2. Guidelines for Photographs 27

A.1 Threat Matrix and Interpretive/QC Methodologies 40

vi

Integrity Data Management and Integration

1. Scope

   
   

   This bulletin provides a compendium of methodologies and considerations for integrating the underlying data used to support integrity management. Any one approach, let alone the entirety of the document, may not be appropriate or applicable in all circumstances. The document reviews possible approaches for consideration by operators in the context of their specific circumstances.

   
   

   The primary focus of this bulletin is the methodologies and processes used to spatially integrate and normalize the data to support the application of comparative techniques used in interpreting integrity data, with particular emphasis on in-line inspection (ILI) data. The document begins with a discussion of general data-quality processes, goals, and considerations such that data quality approaches can be considered in the context of the data integration processes.

   
   

   An impediment to informed integrity decisions is the inability to efficiently review a broad spectrum of data in a format that has been normalized and spatially aligned. With the variations in organizational structures, integrity management programs, and technologies used across the pipeline sector, individual operators design data integration procedures that are customized to their organizational structure, processes, and pipeline systems.

   
   

   Properly managed and integrated data support agile analytics to integrate new data as they become available and to recognize coincident events and patterns. The data source may be from within an organization, or may be external to the company, as in the case of representative data based on industry experience or manufacturing processes. The intent is to empower operators to efficiently analyze and integrate threat- and integrity-related data to support their integrity management programs.

   
   

2. Normative References

   
   

   The following referenced documents are indispensable for the application of this document. For dated references, only the edition cited applies. For undated references, the latest edition of the referenced document applies (including any addenda/errata).

   
   

   API RP 1160, Managing System Integrity for Hazardous Liquid Pipelines

   
   

   API RP 1163, In-Line Inspection Systems Qualification

   
   

   API RP 1173, Pipeline Safety Management Systems

   
   

   API RP 1176, Recommended Practice for Assessment and Management of Cracking in Pipelines

   
   

3. Abbreviations

AC alternating current

ACVG alternating current voltage gradient

BSEE Bureau of Safety and Environmental Enforcement

CFR Code of Federal Regulations

CIS close interval survey

1

2 API BULLETIN 1178

CP cathodic protection

DA direct assessment

DCVG direct current voltage gradient DMA discrete metal loss anomaly DOC depth of cover

ECDA external corrosion direct assessment ERF estimated repair factor

EXT external

FPR failed pressure ratio

GIS geographic information system GPS global positioning system

HCA high consequence area HDD horizontal directional drill ILI in-line inspection

IMU inertial mapping unit INT internal

IT information technology

MAOP maximum allowable operating pressure MFL magnetic flux leakage

MIC microbiologically influenced corrosion ML metal loss

MOC management of change MOP maximum operating pressure MPI magnetic particle inspection MTR mill test report

NAD27 North American Datum of 1927 NAD83 North American Datum of 1983 NDE nondestructive examination

INTEGRITY DATA MANAGEMENT AND INTEGRATION 3

OD outside diameter

POD probability of detection PODS Pipeline Open Data Standard POI probability of identification ROW right-of-way

RPR rupture pressure ratio RTK real time kinematic

SCC stress corrosion cracking SME subject matter expert

SMYS specified minimum yield strength TDC top dead center

TPD third party damage

TQM total quality management UT ultrasonic testing

WB wrinkle bend

WGS84 World Geodetic System 1984

4 Benefits to an Enterprise Data Management System

Managing pipeline integrity data historically involved the rather manual process of populating data within spreadsheets or disparate databases. Transitioning to an enterprise database to manage large pipeline integrity data sets provides an operator with several advantages, including the following:

• Improved auditing and traceability: When spreadsheets are created, the logic and judgment that is applied while an individual is manipulating data is not captured, or easily understood. In most cases, this logic exists only in the mind of the individual who created the spreadsheet, which may result in compliance risk.

  
  

• Improved tracking of data corrections: Propagating corrections to data errors across multiple dependent spreadsheets, or back to the original data sources, is difficult and may potentially introduce further errors.

  
  

• Improved safeguards against human error: Human errors, such as versioning errors and corruption errors, can compromise the integrity of data entry. Databases and their associated graphical interfaces facilitate the implementation of quality rules and constraints that mitigate the potential for human error.

  
  

• Improved resource utilization: Databases may provide improved efficiency over data management that uses disparate spreadsheets.