Make Your Oil and Gas Assets Smarter by Implementing Predictive Maintenance with Databricks - The Databricks Blog

Maintaining assets such as compressors is an extremely complex endeavor: they are used in everything from small drilling rigs to deep-water platforms, the assets are located across the globe, and they generate terabytes of data daily. A failure for just one of these compressors costs millions of dollars of lost production per day. An important way to save time and money is to use machine learning to predict outages and issue maintenance work orders before the failure occurs. Ultimately, you need to build an end-to-end predictive data pipeline that can provide a real-time database to maintain asset parts and sensor mappings, support a continuous application that processes a massive amount of telemetry, and allows you to predict compressor failures against these datasets. Our approach to addressing these issues is by selecting a unified platform that offers these capabilities.