The advent of more advanced methods for monitoring corrosion means that, for most pipeline professionals, decisions are not constrained by a lack of data. If anything, the sheer volume of information available can prove to be a bottleneck, as the amount of time required for analysis grows with the amount of data available. This paper focuses on how pipeline corrosion control departments, integrity management personnel and administrators can obtain a more nuanced view of their corrosion data by using visualization techniques to quickly analyze large datasets. Specifically, the paper examines the key first step in visualizing corrosion data: integrating different types of data from different sources into a single system by using spatial components within each dataset to correlate the information. It will explore the types of data that are well suited for data interactivity analysis, as well as best practices for collecting this data and preparing it for the visualization process.


For the pipeline professional, there is no shortage of data that must be closely analyzed to ensure the safe operation of the system. In this paper, we will focus on the types of analysis performed by Integrity Management and Corrosion Prevention departments that relate to assessing the condition of the pipeline, and how these analyses can benefit from using multiple, disparate types of information as part of the effort.

Performing any type of effective analysis requires a collation of large datasets that are not necessarily subject to easy alignment. Part of the difficulty relates to how the underlying data is collected in the first place. Whereas some pipeline assets are part of distributed control systems and are scrutinized in near-real time, with data from different monitoring equipment collected and returned simultaneously, assessing other areas of the system with the same precision means using data from a variety of sources collected at different times. For these assets, taking stock of cathodic protection, identifying the presence of leaks, and assessing the condition of the pipeline means gathering information that is collected using different methodologies and returned in different formats. To obtain a comprehensive understanding of the health of the pipeline system, an analysis using all available data should be conducted on a regular basis. However, the disparate nature of the data itself make this a difficult operation. It is often a painstaking operation, requiring the user to stitch the data together by hand, one dataset at a time. As a result, such operations may be conducted less frequently than companies might wish.

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