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.
The more time we spend with our clients the more we learn they are reliant on spreadsheets. To this point, we’ve jokingly considered changing our mission from “Predict pipeline failures, save lives and protect the environment… with the assistance of Machine Learning” to “We eliminate legacy Microsoft Excel spreadsheets”. Of course, all joking aside, we want to enable integrity management teams to spend their time doing high-value engineering. The latest Microsoft Excel sheet we have replaced is a customer’s version of a Crack Fatigue Analysis based on the modified Ln-Sec equation for cracks in pressurized pipelines.
During our time in the Microsoft Accelerator, Data Science, and Machine Learning cohort, we interviewed a few folks working in integrity management for pipeline operators to ask them to describe some of their most difficult challenges. We anticipated it would range from dealing with silos of data to spatially integrating risk data. However, we were surprised to learn that they simply wanted a solution that would accurately align features (welds, anomalies, valves, etc.