A new Redbook, Reducing refinery downtime with IBM smarter asset management for oil and gas (SAM-OG) by Jenny Li and Paul Peters provides a succinct (18 page) overview of how IBM is leveraging what has become known as the ‘internet of things’ and how its consultants go about assessing asset data integration issues and propose specific products and configurations from IBM’s burgeoning application line-up.
At the base of the stack, SAM-OG uses IBM’s internet of things foundation (now also referred to as the IBM Watson IoT Platform) to connect real-time data from field-based sensors to IBM’s flagship enterprise systems, Maximo for asset management and Scheduler for turnaround. Time series sensor data can be staged in a data historian such as OSIsoft’s PI System or streamed into the system using a publish/subscribe model. Data is augmented with maintenance logs, production reports and more to enable predictive analytic and optimization models.
IBM InfoSphere Streams can be deployed to support real-time analytics of process data streams including from producing fields. IBM Cognos can also be used to provide reporting and business intelligence. Inventory analytics can also leverage IBM SPSS, running in the cloud, to profile inventory, predict out-of-stock conditions, and reduce overstocking. The solution can be deployed in the cloud or on premises.
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