Offshore assets face increasing pressure to reduce deferrals, maximize margins and reduce health and environmental incidents. Risk in upstream organizations is shifting from exploration to production, increasing expectations for running world class operations. Oil and gas leaders are adjusting to this new reality.
Unfortunately, the company’s digital transformation campaign fell behind schedule. The problem wasn’t data analysis but collecting and managing the data in an efficient, scalable, accurate way. In most cases, the company was already gathering and analyzing needed data, but the efforts were generally localized, inefficient and didn’t scale. Technicians and engineers produced and maintained endless spreadsheets. Or they developed bespoke applications for narrow, local objectives that didn’t help technicians elsewhere. For example, if a technician identified equipment in Africa that needed more frequent maintenance to run optimally, the technician in the Caribbean, overseeing the same equipment, couldn’t benefit from the insight.
Senior technician – “Getting data from other teams is hard. We’ll ask for compressor data, but it won’t come back for weeks, and is incomplete. That back and forth saps people’s energy and morale.” Without a 360-degree view of operations-related data, insights remain out of reach.
These challenges impacted three key use cases in phase 1 of the transformation:
- Harmonized Enterprise Data − Creating a uniform enterprise-wide data structure for data generated by target equipment across all assets.
- Real Time Anomaly Applications − Dozens of applications needed to be created to utilize real time data from its historian system. What was missing was a harmonized, flexible data hierarchy related to production equipment. This would allow the company to holistically assess the performance and health of target equipment, instruments, controllers, processes and fluid monitoring across all offshore assets.
- GE Predix Asset Performance Management (APM) application − The company was investing millions of dollars in GE’s Predix APM application. However, they struggled to bring process data onto the platform in a usable manner, slowing the APM deployment.