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 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.
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.
Following our procurement of Phillip’s 66 on-premise solution, we’ve been busy migrating it to the Microsoft Cloud in preparation for our Private Preview program. Through our Private Preview program, we plan to launch a minimally viable product (MVP) into the market worldwide in Q4 of this calendar year. Typically, we would be conducting several whiteboard sessions with customer journeys; however, through demos we have done with prospective customers thus far, we are certain that the solution already has the appropriate business processes in place.
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 the industry has used for decades. However, this time, instead of sending data from the pig to a roomful of analysts and waiting months for results, the company applied a solution based on machine learning and data visualization.