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.
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.
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 of endless errors. However, by training a machine learning data classifier on enough log data, this task becomes the perfect scenario where data science can drastically improve integrity management practices.
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 everything on a project basis. It’s simply the way things have always been. Outside of data analysis this process probably makes sense.
Episode Description Dwayne Kushniruk,Tim Edward, and Brandon Taylor, from OneBridge Solutions, a subsidiary of OneSoft Solutions, talk with host Jordan Goodman about OneBridge Solutions game changing approach to helping predict and eliminate potential oil and gas pipeline problems. Through the power of big data and machine learning, OneSoft’s new SaaS approach to anticipating and preventing pipeline failures has the potential to be a game changer in the fuel industry. Kushniruk, Edwards and Taylor explain their company’s innovative high powered approach and the investment opportunities offered.