Pipeline Integrity Management and Data Science Blog

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...

Crack Fatigue Analysis as a Cloud Service

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,...

Disrupting the ingestion, feature alignment and classification process

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...