Currently, inline inspection in the oil and gas sector is a series of staged engineering events, designed to assess the condition of pipeline assets. It is costly, infrequent, and only addresses those lines that are within reach. In fact, it is estimated that 40% of the world’s pipelines are unpiggable. The vulnerabilities between inspections,which are typically years apart, or the inability to conduct inspections at all, puts the world’s aging pipeline infrastructure at risk. In order to address these issues, we have focused on changing the economics, the technology, and the workflow associated with inline inspection. We have introduced the industry’s first self-serve inspection model that eliminates significant engineering and downtime costs. We have developed miniature, multi sensor Pipers® technology that freely flows within any and all active pipelines, regardless of location, condition, material or configuration. This technology is capable of inspecting pipelines as small as 2 inches in diameter, and surveying for leaks, geometric defects, magnetic anomalies, and deposits in a single run. Further, we have reduced the cost of inline inspection by a factor of 10, making it possible for integrity programs to be more present, more often, across all of their assets. Finally, such accessible technology has given us the opportunity to work with a complete range of clients – large and small, private and public – resulting in rich datasets libraries that offer unprecedented contextual analysis and continuous advancement of our data analytics capabilities. Our approach provides clients with uninterrupted access to up-to-date condition-critical data, allowing them to optimize planning, decision-making, and actions.

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INGU

John van Pol

Amanda M.

Yvonne van den Berg

Michelle Straat

Anouk van Pol

The Data Science Team at INGU leverages advanced analytics and machine learning techniques to analyze the rich datasets generated from our innovative inline inspection technology. Their primary focus is on extracting actionable insights from pipeline condition data to enhance predictive maintenance, improve safety measures, and optimize operational efficiencies. By continuously refining algorithms and models, the team aims to empower clients with data-driven decisions that mitigate risks and streamline pipeline management processes.

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