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Case Study:
Optimizing Yield for Digital Oil Fields

By downloading this case study, you will learn how a large oil production company improved its oil production rate and reduced downtime for Electrical Submersible Pumps (ESP) by using DataRPM’s CPdM platform.

Business Challenge:

The client, a large oil production company based in the Middle East, wanted to better understand its Electrical Submersible Pumps. By being able to predict ESP failures ahead of time, the client could increase its overall production output and minimize service disruptions, optimizing oil yield in the process.

What DataRPM Did:

DataRPM’s CPdM platform helped the client: 

  1. Accurately predict oil productivity
  2. Optimize oil yield

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About DataRPM

DataRPM is an award-winning predictive analytics company focused on delivering the next generation predictive maintenance solutions for the Industrial IoT. DataRPM platform automates data science leveraging the next frontier in machine learning known as meta-learning, which is machine learning on machine learning. The platform increases prediction quality and accuracy by over 300% in 1/30th the time and resources delivering 30% in cost savings or revenue growth for business problems around predicting asset failures, reducing maintenance costs, optimizing inventory and resources, predicting quality issues, forecasting warranty and insurance claims and managing risks better.


Industry Recognitions and Awards
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