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,…
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 were…
Data Ingestion and Normalization – Machine Learning Accelerates the Process
If you have ever looked through 20 years of inline inspection tally sheets, you will understand why it takes a machine learning technique (e.g. random forest, Bayesian methods) to ingest and normalize them into a database effectively. It would be a monumental task if attempted manually by a human … not to mention the risk…
Transforming pipeline integrity management through data science
It seems that every time an article or presentation is published highlighting some fancy new improvement to oil & gas pipeline integrity management related to inline inspection (ILI) and external data analysis, it comes attached with professional services to operationalize it. This is not so surprising, since historically the industry has been conditioned to compartmentalize…
