Case study: Predicting maintenance and production in oil wells

In a business where unexpected failures and unscheduled downtime are both costly and dangerous, the ability to predict failures in critical assets before they occur is paramount.

Maintenance operations on wells can range from minor failures of surface equipment to major problems that require full workovers, such as fixing parted rods and leaking tubing. Read this case study to learn how Darwin-generated models predicted workover, rod change, and cleaning operations needs.

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