Cutting Downtime in Half with Deep Neural Networks

Challenge Revolutionizing downtime reduction for a global energy giant Our client, one of the largest integrated energy companies in the world, needed a way to shorten downtimes by proactively identifying breakdowns before they happened. The oil wells’ remote locations and the pumps’ depths compounded the challenge of addressing failures quickly. Traditional maintenance practices typically involve periodic inspections or scheduled maintenance at fixed intervals, which can be time-consuming or not viable when a well is continuously running. Unplanned failures are common and can lead to extended downtime, further impacting productivity and profitability Addressing the unforeseen Unplanned failures of ESPs were a critical problem for our client. These unexpected breakdowns cause significant downtime, impacting production schedules and operational profitability. Minimizing downtime is incredibly important to avoid multi-million-dollar losses, primarily due to the weeks it takes to schedule, travel to, and repair non-producing wells.

Smart ESP management: reducing downtime, boosting ROI

Periodic replacement and fixed-interval maintenance are not profitable when doing so requires stopping the production of a well for potentially no reason. If an ESP was found to be running fine and had plenty of life left, the well experienced significant downtime (and lost revenue) for little to no return. Our client needed a more intelligent and proactive approach. The goal was to allocate maintenance resources just in time to schedule replacement before an ESP failure occurs, optimizing maintenance costs and ESP lifetime value and reducing unplanned downtime.

© 2023 Fractal Analytics Inc. All rights reserved

3

Made with FlippingBook - PDF hosting