Learnings From Data Management and Integration Efforts on the Enbridge Pipeline System

Abstract

Enbridge Pipelines Inc. operates one of the longest and most complex pipeline systems in the world. A key aspect of the Enbridge Integrity Management Program (IMP) is the trending, analysis, and management of data collected from over 50 years of pipeline operations. This paper/presentation describes Enbridge’s challenges, learnings, processes, and innovations for meeting today’s increased data management/integration demands. While much has been written around the premise of data management/integration, and many software solutions are available in the commercial market, the greatest data management challenge for mature pipeline operators arises from the variability of data (variety of technologies, data capture methods, and data accuracy levels) collected over the operating history of the system. Ability to bring this variable data set together is substantially the most difficult aspect of a coordinated data management effort and is critical to the success of any such project. Failure to do this will result in lack of user confidence and inability to gain “buy-in” to new data management processes. In 2001 Enbridge began a series of initiatives to enhance data management and analysis. Central to this was the commitment to accurate geospatial alignment of integrity data. This paper/presentation describes Enbridge’s experience with development of custom software (Integrated Spatial Analysis System – ISAS) including critical learnings around a.) Data alignment efforts and b.) Significant efforts involved in development of an accurate pipe centreline. The paper/presentation will also describe co-incident data management programs that link to ISAS. This includes enhanced database functionality for excavation data and development of software to enable electronic transfer of data to this database. These tools were built to enable rapid transfer of field data and “real time” tool validation through automated unity plots of tool defect data vs. that measured in the field.