Pipeline Integrity Management and Data Science Blog

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...

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 ...

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...