Pipeline Integrity Management and Data Science Blog

Developments with Cognitive Integrity Management

We are excited to share the most recent progress that has been made to our Polaris release set for launch before the end of the year. Our data science and development teams are busy working through user stories for our Polaris minimally viable product (MVP) release. At the end of May we held...

Alignment Process: Elevate the Accuracy and Reliability of your current efforts

Alignment Business Problem:

When evaluating the integrity of a pipeline using inline inspection data, one of the primary challenges the integrity engineer faces is reliably and accurately aligning data from consecutive inspections with other asset information. Without this alignment, both longitudinally along the length of the pipeline and by clock position, it is extremely difficult...

Add Significant Value to Your Risk Analysis with Cognitive Integrity Management

Two of the most significant challenges in performing quantitative pipeline risk analyses include the lack of complete and reliable datasets and not having the ability to properly align and integrate this data into the pipeline risk assessment. In this post, we will discuss the role of Cognitive Integrity Management in transforming quantitative risk analysis...

How machine learning contributes to smarter pipeline maintenance

Machine learning can allow oil and gas companies to make better use of the enormous amounts of data as they try to maintain their pipelines.

Last January, a major oil and gas company ran routine inspections of its thousands of miles of pipeline, using the same basic robotic device—the pig—that...