I am routinely asked by pipeline operators what we can offer them regarding predictive analytics for pipeline integrity. Usually, these questions are driven by a common-sense need to reduce operational costs while maintaining safely operational pipeline infrastructure. Occasionally, such requests stem from a fear of missing out on the marginal gains data analytics can provide. Invariably, my response to them begins with a discussion on data governance, systems and processes, and data management. The truth is that it is impossible to scale up to more advanced analytics models without a solid foundation in these key areas. In the pipeline industry, data management is crucial to ensuring pipeline efficiency, safety, and profitability; however, it is all too often overlooked.
Disturbing real-world consequences
The starkest reminders of data management’s importance are found in historical failures to identify integrity threats and respond effectively. We have seen countless such failures, from leaks and ruptures to cybersecurity breaches, many of which were catastrophes caused in part by missing or inaccurate safety information and pipeline records, resulting in tragic losses of life and property, to say nothing of environmental damage.
In more recent times, a 2021 ransomware attack on the largest fuel pipeline in the USA demonstrates the growth of a new class of threat to pipeline integrity and security, against which the industry must further strengthen its defenses.
In our everyday business working with pipeline operators worldwide, we regularly find situations where missing or inaccurate pipeline data results in ILI tool speed excursions and compromised inspection data quality, or in the worst cases, stuck tools that have to be cut out, resulting in significant supply disruptions.
The criticality of data management
Data management in the pipeline industry involves collecting, storing, and analyzing data related to the construction, operation, and maintenance of pipelines. This data includes everything from geological surveys and environmental impact assessments to real-time monitoring of pipeline conditions. Effective data management ensures that this vast repository of information is accurate, actionable, and immediately accessible.
Arguably the primary benefit of good data management is improved decision-making. When data is well-organized and easily accessible, pipeline operators can make informed decisions quickly, thus maximizing operational efficiency, reducing downtime, and cutting costs. This is even before we consider implementing predictive maintenance systems to help operators identify potential issues before they become major problems, preventing costly repairs and minimizing the risk of pipeline failures.
On the other hand, poor data management practices can lead to missed opportunities and suboptimal decisions. For example, many organizations store their data in silos – disparate, isolated systems that cannot communicate with each other. This form of data storage can make it difficult to get a comprehensive view of the pipeline operations and condition. If maintenance data is not integrated with operational data, for instance, it can be challenging to identify patterns that indicate potential issues.