In aging facilities, maintaining pipeline integrity through proactive maintenance is vital. A data-centered approach allows stakeholders to prioritize critical assets, allocate resources efficiently, and ensure proactive integrity-maintenance measures. The novel approach presented in this study integrates massive amounts of data that affect pipeline integrity by providing visible analysis of all findings from different inspection techniques, thus prioritizing inspection and repair programs while minimizing downtime and disruption.
Introduction
The operator’s inspection and operational data-management software and its database faced limitations in integrating and communicating multiple data sources monitoring asset degradation, such as thickness-monitoring locations (TML), corrosion-inhibitor programs, smart pigging data, topography, cathodic protection (CP) surveys, direct current voltage gradients (DCVG), close-interval potential surveys (CIPS), laboratory results, and bacterial activity. To overcome this technical boundary, a central data-management system is proposed that uses Microsoft Power BI along with OSI PI Coresight to build graphs and patterns that communicate relationships between data points.
Methodology
In-Line Inspection (ILI) Analysis and Topographical Correlation. Correlation of two ILI data sets from different vendors is a time-consuming process, especially when a massive field is involved that has generated thousands of records.
