One of the most important areas where leveraging live instantly accessible data can provide clear short term and long term gains are Condition Monitoring (CM) and Condition Based Maintenance (CBM) of the subsea equipment. When leveraging instantly available times series data from IIOT sensors which are mapped to context data (sensor metadata, process diagrams, events, etc) one can track deterioration mechanisms of the subsea equipment remotely and plan interventions in advance in order to assure minimum downtime. A planned intervention may give a 1-day shutdown and allow for several planned concurrent activities to be executed, while an unplanned can give a 30-day shutdown. Any advance warning that equipment is about to fail thus results in shorter shutdown, prepared and planned activities, and less deferred production. However, in order to achieve this, a strong and scalable infrastructure with necessary APIs needs to be in place. A key for setting up advanced visualizations for live monitoring of the equipment is the ability to easily access contextualized data using such APIs.

The presentation will focus on the technological foundation within a data platform that provides scalable access to sensor and all related data and how live data and real-time visualizations, as well as ML algorithms, are leveraged to both optimize operations and optimize maintenance of subsea equipment.