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Late-life assets challenge the balance of maintaining uptime and asset integrity while meeting regulatory compliance and production targets. This challenge needs to be met by the operators and asset owners while managing their OPEX to maintain profitability.
VROC was engaged by an operator in early 2020 to use the AI Platform to remotely monitor the health of their offshore platform in the North Sea to better understand reliability issues.
During the project period, due to increased restrictions caused by the pandemic, only personnel critical tothe performance of the asset could go offshore. This meant that among others, Reliability Engineers were restricted to working from home and would normally have no direct visibility of asset performance. Maintenance crew not directly related to requirements offshore were also subject to these restrictions which would have potentially effected asset performance.
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 key asset performance and reliability engineers were able 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 graphic representations of asset health, with the ability to drill down to find contributing factors and root causes for any significant deviations from normal operations, helped in the early identification of future problems.
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.
Maintenance is being reorganised to minimise firefighting and transition to a risk based approach based on predicted failures, allowing better shutdown planning
As the operator now has better visibility on their equipment, their focus has shifted to optimising individual processes using complex models
Direct and immediate cost savings have come from having fewer POB and reduced maintenance; however, the significant savings have resulted from the mitigation of failures and optimisation of processes.
Plant 1 US$1.8M reduction in production loss, $1.4M savings due to reduced downtime, 83% reduction in production loss
Plant 2 US$431,000 reduction in production loss, 80% reduction in production loss
As these examples illustrate, significant benefits that have been directly derived by the late-life asset operator during one of the most challenging years for the oil and gas industry. The direct correlation with a reduction in OPEX is evident and often immediate with the deployment of the VROC machine learning modelling techniques on our AI platform.
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The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
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