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TotalEnergies predicts pump failure with 95% accuracy

Within the oil and gas industry, electrical submersible pumps (ESPs) are used to lift moderate to large volumes of fluid out of wells. Similar to other rotating machinery like compressors, turbines or different pumps, ESPs are operated under very specific conditions and certain parameters need regular monitoring to ensure the pumps stay in optimal condition. Vibration analysis, in particular, is used to determine if rotating equipment is working as it should.

Performing maintenance on ESPs can be very costly, as it usually requires stopping production, but pump downtime is also expensive when you add up the cost of lost output  and equipment repair. In addition, ESPs themselves are big-ticket items, so regular maintenance is critical to make sure they continue to work correctly and last as long as possible.


“It’s difficult for production engineers to know which wells to focus on. On certain projects, we may have up to 30,000 data points, so it’s hard to detect where the problems are and predict when they are going to occur. Fieldbox took all the data we had collected, looked for correlations in the numbers and identified key parameters to build a highly accurate pump failure prediction model.”
Cédric Picher Wells Performance Engineer, TotalEnergies

The problem with preventive maintenance

Many oil and gas companies take a preventive approach to ESP maintenance, replacing parts and scheduling maintenance at predetermined intervals to avoid potential breakdowns, but these schedules are based on statistical averages that don’t necessarily reflect the specific operating conditions or usage history of the pump in question. And since purchasing replacement parts and stopping production both come with a hefty price tag, this “whether it needs it or not” approach is not necessarily the most cost-effective strategy when it comes to pump maintenance.

TotalEnergies knew if they could find a way to identify and follow variables that reliably indicated a specific pump was likely to fail, they could save money by taking a predictive rather than preventive approach to maintenance, undertaking the cost of replacing parts and scheduling repairs only when necessary.
This is what Fieldbox set out to help the oil and gas company do.

Deploying advanced algorithms to predict pump failure

Fieldbox gathered contextual data from electrical submersible pumps located in four of TotalEnergie’s wells, as well as 100 sensors located in wellbores and around wellheads. It then deployed its “ESP Failure Prediction” solution for TotalEnergie’s complex onshore and offshore assets, using the history of multivariate data streams to detect the appearance of weak signals early on.

Fieldbox used machine learning algorithms to identify the warning signs of pump failure early enough to replace a part or perform maintenance before a breakdown happened, avoiding unnecessary downtime and reducing or eliminating the need for a full repair altogether.

An operational dashboard with a real-time overview of these key variables was created and is now available to operations staff 24/7, allowing them to anticipate trends and drill down into data to investigate operational issues in detail. 


Save Money and Improve Employee Morale and Safety

Improve operational efficiency and avoid subjecting your team to unnecessary stressful or emergency situations that could also pose a safety risk.

Order Replacement Parts Ahead of Time

Optimize your purchase price and avoid getting caught off guard by ordering replacement pieces before you urgently need them.

Reduce the Need for Repairs

Minimize unnecessary wear and tear on machinery so that equipment needs fixing less frequently.

Optimize Equipment Efficiency

Schedule maintenance for low-demand periods when equipment is less needed, and extend the lifespan of machinery by using it more efficiently. 

Optimizing operational efficiency, thanks to AI

With Fieldbox’s predictive model, TotalEnergies is now able to accurately identify when there are anomalies in how an ESP is behaving and, based on historical data, alert the operations team between 10 to 40 days in advance of when problems are likely to arise. This ability to detect and predict ESP failure early on with more than 95% accuracy has greatly improved the company’s overall operational efficiency in multiple ways.

With smart data insights and alerts,  the Well Performance team can focus their attention on wells that are showing potential signs of trouble and circumvent problems or dangerous situations before they happen, improving worker safety and extending the run life of ESPs installed in those wells.  Having plenty of advance notice also gives them the opportunity to schedule any required maintenance at times when it will least interfere with production.

In cases where ESP failure is unavoidable or very likely to happen, the team is able to prepare for the inevitable, procuring any necessary parts or equipment, and reorganizing logistics to find a suitable workaround that will limit production losses as much as possible.


A brighter future for energy

Using smart technology to extend the life of equipment and improve worker safety fits into TotalEnergies bigger goals to become a more sustainable energy player. As the company moves forward, their goal is to keep working with companies like Fieldbox to find new and better ways to improve the way they work and transform themselves into an innovative multi-energy player that is taking the lead on transitioning to more sustainable energy solutions.