Renewables | Oil & Gas | Nuclear | Metals | Mining

Energy & Mining

High stakes and the potential to revolutionize operations

Among some of the most capital-intensive industries, the extraction and energy sectors face a number of specific, high-stake challenges.

  • Production cycles are long and yield is highly variable.
  • Reliance on heavy equipment means costs skyrocket when unplanned downtime occurs due to equipment failure or unplanned maintenance.
  • Operators are often dealing with extreme hazards.
  • Geographically remote locations are hard to access, which greatly increases the cost and lead time of moving people, materials, fuel and consumables.
  • The ecological footprint of these sectors is high and environmental and emissions regulations are increasingly restrictive.

AI for Energy and Mining

Control and monitor yield and quality in real time

With oversight and control, you can give experts and operational users access to the same data, identify opportunities for efficiency and yield improvement, and reconcile field and office data. See major improvements in visibility and communication of operational KPIs, and improve reporting and alerting processes.

Better manage your fleet

Improve productivity and efficiency by providing operators with the information and analytics they need to track assets, optimize assignments and minimize operational risk.

Optimize processes and equipment use

Control how machines react in varying conditions to optimize uptime and yield, extend the working life of capital equipment, and improve uptime.

Safety & risk detection

Use sensor (IoT) and image data to monitor conditions remotely with less need for travel or putting personnel at risk.

Predictive maintenance

Avoid the cost and inconvenience of unplanned maintenance.

Optimize power and water usage

Reduce costs while improving your environmental stewardship.

“The difficulty for production engineers is to focus on the wells with problems. For certain projects, we may have up to 30,000 data points. It’s hard to detect where the problems are, and to anticipate them. We gave data to Fieldbox so they could build the model. They have been able to check correlations and identify key parameters to produce very interesting results.”
Cédric Picher Wells Performance Engineer, TotalEnergies

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