Pipeline Integrity Management and Data Science Blog

With AI, zero failure is more than a pipe dream

Everyone in our business knows—or ought to know—about the pipeline maintenance crisis that puts billions of dollars, lives, property, and the reputation of midstream oil & gas industry at risk, leading some in the public to call it a “ticking timebomb.” Statistics indicate tens of thousands of miles of pipes decades beyond their predicted end-of-life, scattered so wide and buried so deep that just finding them on a map can be a problem.

Cognitive Integrity Management™ 3.0 General Availability

This week we officially released Cognitive Integrity Management™ 3.0, (“CIM 3.0”), which was developed under the product codename Polaris. The product team has been working tirelessly with SME’s from our participating pipeline operators during our private preview to ensure the solution applies horizontally across most operators. Starting at the end of May, each week these SME’s provided us operational data from their existing systems, assisted to validate CIM features, and confirmed the accuracy of the results.

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 our kick off meeting with operators participating in our Private Preview at the Microsoft Technology Center in Houston.

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 to make comprehensive comparisons of identified features between multiple Inline Inspections. To correctly handle this comparison, the process used must first align anomalies and do so with a high quantifiable level of statistical confidence.

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 to address these challenges when looking at ILI anomalies and repairs. Current Problem with Risk Analysis Processes: A significant limitation seen in many risk assessments is the incomplete use of ILI data.