Reliability personnel working remotely due to COVID-19 lockdown are able to track asset health in real time and put in place mitigation strategies to avoid shutdowns

Benefits at a Glance

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Early identification of future problems for improved planning

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Continued production and improved asset integrity despite a lower personnel on board (POB)

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Reduced OPEX from fewer personnel on board and reduced maintenance


VROC was engaged by an Oil and Gas operator in early 2020 to use it's artificial intelligence and machine learning predictive analytics platform to remotely monitor the health of their offshore platform in the North Sea to better understand reliability issues.

During the engagement the COVID-19 pandemic restrictions limited access to the offshore asset to only critical personnel. This meant that among others, Reliability Engineers and Process Teams were restricted to working from home and without any direct visibility of asset performance. In addition some Maintenance crew not directly related to requirements offshore, were also subject to these restrictions.

The operator needed to adapt to the new working arrangements, whilst ensuring the assets uptime, integrity, regulatory compliance and production targets were all maintained.


Within a three-week period, VROC was able to ingest the historical data and start downloading live data from their historian through a secure data connection.

After a week’s training on building models and dashboards and with support from VROC data scientists and engineers, the operator’s reliability and operations engineers were able to build their asset health monitoring models to focus on their own areas of interest and provide their management and teams with live and forecasted asset health information using custom built dashboards.


The VROC AI platform allowed the operator’s key asset performance and reliability engineers to deploy AI models and visualisation dashboards on critical assets e.g., compressors, generators, and water injection pumps to monitor their health and performance. These models helped in the early identification of future problems. The right maintenance personnel with the correct spares were able to be mobilised only when necessary to affect repairs, while any mitigation was done by operations personnel already on board the platform.

This has resulted in the operator being able to continue production with the requisite level of asset integrity and performance despite a lower personnel on board (POB) offshore. With each successive positive result, the customers confidence in using this model has risen to the point that it has now become standard operating procedure for reliability and maintenance operations with a long term reduced offshore head count.

Direct and immediate cost savings have come from having fewer POB and reduced maintenance during the engagement. The actual reduction in OPEX figures have not been made available by the customer, but can be inferred and are bound to have a long-term effect due to the change in the O&M philosophy with the use of the AI platform.

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