Cutting Downtime in Half with Deep Neural Networks

Outcome Maximizing Uptime and Profitability

Instantaneous Impact Implementing the predictive maintenance model resulted in a substantial reduction in unplanned downtime. By identifying equipment issues at least two weeks before they escalated, the company could schedule maintenance in time to get a team out to the remote site before the ESP failed, leading to additional revenue generated and optimized maintenance operations. Sustainable Gains Over time, the benefits of this solution will continue to grow. In particular, the model’s ROI will continue to improve as additional failure data is generated. This will reduce or eliminate downtime before a team can repair a well. It will also decrease false positives for pumps with remaining useful life. This would help see a substantial revenue increase from the additional uptime of pumps in the field.

Profitability Enhancement

Cost Savings & Revenue Growth

Downtime Reduction

The model significantly reduces the downtime between a failure and a response, resulting in additional revenue.

Knowing what has failed and where leads to reduced turnaround time and increased profitability.

Substantial cost savings and revenue growth are realized through intelligent and proactive maintenance.

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