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API Bull 1178 Integrity Data Management and Integration Guideline, First Edition
standard by American Petroleum Institute, 11/01/2017
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API BULLETIN 1178
FIRST EDITION, NOVEMBER 2017
American Petroleum Institute (API) publications necessarily address problems of a general nature. With respect to particular circumstances, local, state, and federal laws and regulations should be reviewed.
Neither API nor any of API’s employees, subcontractors, consultants, committees, or other assignees make any warranty or representation, either express or implied, with respect to the accuracy, completeness, or usefulness of the information contained herein or assume any liability or responsibility for any use, or the results of such use, of any information or process disclosed in this publication. Neither API nor any of API’s employees, subcontractors, consultants, or other assignees represent that use of this publication would not infringe upon privately owned rights.
Classified areas may vary depending on the location, conditions, equipment, and substances involved in any given situation. Users of this standard should consult with the appropriate authorities having jurisdiction.
Users of this standard should not rely exclusively on the information contained in this standard. Sound business, scientific, engineering, and safety judgment should be used in employing the information contained herein. API is not undertaking to meet the duties of employers, service providers, or suppliers to warn and properly train and equip their employees, and others exposed, concerning health and safety risks and precautions, nor undertaking their obligations to comply with authorities having jurisdiction.
Information concerning safety and health risks and proper precautions with respect to particular materials and conditions should be obtained from the employer, the service provider or supplier of that material, or the safety datasheet.
API publications may be used by anyone desiring to do so. Every effort has been made by API to assure the accuracy and reliability of the data contained in them; however, the API makes no representation, warranty, or guarantee in connection with this publication and hereby expressly disclaims any liability or responsibility for loss or damage resulting from its use or for the violation of any authorities having jurisdiction with which this publication may conflict.
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Copyright © 2017 American Petroleum Institute
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This document was produced under API standardization procedures that ensure appropriate notification and participation in the developmental process and is designated as an API standard. Questions concerning the interpretation of the content of this publication or comments and questions concerning the procedures under which this publication was developed should be directed in writing to the Director of Standards, American Petroleum Institute, 1220 L Street, NW, Washington, DC 20005. Requests for permission to reproduce or translate all or any part of the material published herein should also be addressed to the director.
Generally, API standards are reviewed and revised, reaffirmed, or withdrawn at least every five years. A one-time extension of up to two years may be added to this review cycle. Status of the publication can be ascertained from the API Standards Department, telephone (202) 682-8000. A catalog of API publications and materials is published annually by API, 1220 L Street, NW, Washington, DC 20005.
Suggested revisions are invited and should be submitted to the Standards Department, API, 1220 L Street, NW, Washington, DC 20005, standards@api.org.
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Integrity Data Management and Integration
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.
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
AC alternating current
ACVG alternating current voltage gradient
BSEE Bureau of Safety and Environmental Enforcement
CFR Code of Federal Regulations
CIS close interval survey
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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
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.