Authors: Edmund Bennett
Digitalization of the pipeline industry and the growing amount of data also increase the need for robust data management practices. Our expert, Edmund Bennett, Principal Data Scientist at ROSEN Group takes a closer look at the aspects that make data management increasingly important for pipeline operators.
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.
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.
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.
Effective data management includes robust cybersecurity measures to protect sensitive information from unauthorized access and cyber-attacks. Without these measures, pipeline operators are vulnerable to data theft, operational disruptions, and reputational damage.
“Culture eats strategy for breakfast,” as the oft-touted Peter Drucker quote tells us – so what better way to succeed than to foster a data-driven culture? This involves not only implementing the right technologies but also ensuring that all employees understand the importance of data and are trained in its best practices. A data-driven culture encourages continuous improvement and innovation, leading to more efficient and effective pipelines.
Organizations must also establish clear data governance policies that define how data is collected, stored, and applied. These policies should include guidelines for data quality, security, and privacy, as well as procedures for regular audits and reviews.
Regulatory compliance is a necessity for pipeline operators. The industry is subject to stringent regulations that require meticulous record-keeping and reporting. Robust data management practices ensure that all necessary data is accurately recorded and readily available for audits and inspections. This not only helps in avoiding high fines and legal issues but also builds trust with regulatory bodies and the public.
One of the most compelling benefits of effective data management is its ability to leverage advanced techniques such as integrity analytics for pipelines and related assets. By utilizing data analytics, pipeline operators can gain deeper insights into their operations, identifying trends and uncovering opportunities for improvement. For example, analytics can be used to optimize routing and scheduling, reducing fuel consumption and emissions. It can also help in forecasting demand, allowing operators to adjust capacity proactively and efficiently.
Moreover, data analytics can enhance risk management strategies. By analyzing historical data and trends, operators can better understand potential risks and develop more effective mitigation plans. This proactive approach to risk management not only enhances safety but also improves the overall reliability of pipeline operations.
As the pipeline industry continues to adopt digital technologies, the need for robust data management practices has become increasingly evident. As experts in integrity management software and practice, with years of experience handling very large volumes of data that we generate and supporting operators with data management, we at ROSEN have the knowledge and tools to guide operators through the complexity of data management options towards an effective solution, from Pipeline Integrity Management Systems (PIMS) governance to powerful solutions such as NIMA Integrity Management and Analytics.