Integrity Analytics

For decades, in-line inspection technologies have been collecting data on pipelines from all over the world. But what can be achieved when vast quantities of information are collected? Our Integrity Analytics initiative investigates how artificial intelligence techniques can support pipeline operators with their comprehensive asset integrity management. As partners with our clients, we deliver unique insights that enable reliable decision-making for the safety, lifetime and performance of their assets.

Key advantages

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Condition benchmarking against the world network or selected reference groups

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Integrity management system effectiveness analyses

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Condition prediction for uninspected pipelines

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Degradation rate prediction

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Threat specific probability functions per threat for quantitative risk assessment


Anticipating a wealth of valuable information

Although we are in the business of pipeline inspection, we are aware that approximately 40 percent of the world's pipelines cannot be inspected using in-line systems, even when we deploy new technologies. We are also aware that there is a wealth of valuable information available for many of the 60 percent that can be inspected. Naturally, this leads us towards supervised machine learning as a monitoring solution. By analyzing trends in inspected pipelines, we leverage this knowledge to enhance monitoring strategies for uninspected pipelines.

The Integrity Data Warehouse

Our Integrity Data Warehouse (IDW) is a robust repository, holding data from in-line inspections of over 26,000 pipelines, covering more than 800,000 km across various global locations. Geospatial data provides additional insight into the characteristics of the local environments of inspected pipelines in the IDW. These global geo-enrichment data is applied to each pipeline in our database and includes soil properties, land usage information, temperatures, and precipitation levels and the intersections of pipelines with relevant infrastructure such as roads, railways, powerlines, and waterways. This extensive data provides us with an unparalleled, broad picture of the condition of a significant portion of the world's piggable pipeline assets. The assets vary across multiple dimensions, including age, coating, diameter, wall thickness, and of course environment. Our expertise in machine learning, combined with this extraordinary dataset, empowers us to extrapolate trends from these inspected lines. We can then make accurate predictions about the integrity of completely unseen pipelines. Our meticulous approach to model training, testing, and validation ensures the models we use provide realistic and reliable predictions.

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Predicting the external corrosion condition of uninspected pipelines

As part of Integrity Analytics, we have recently expanded our predictive analytics capabilities by adding a powerful new service to its portfolio. This service is specifically designed to deliver an accurate prediction of pipeline integrity with respect to external corrosion. It provides rapid, reliable screening of onshore pipeline networks, enabling targeted detailed studies and inspection. Particularly effective for unpigged or unpiggable pipelines, this service leverages a comprehensive database of corrosion features from pipelines worldwide, known as the Integrity Data Warehouse (IDW).

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Assessing threats and their likelihoods

Current codes for hydrogen pipelines are more restrictive than their natural gas equivalents regarding the design and integrity management requirements. Hydrogen is known to reduce fracture toughness and increase fatigue-crack growth rate and therefore presents a challenge for pipeline integrity management. Through our Integrity Data Warehouse, pipelines with a higher likelihood of containing significant cracking can be predicted based on factors such as design and construction, environmental conditions, operational history and available survey data.

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