FORESIGHT’s data-driven models are trained using sensor time-series of historical failures and entail advanced data processing, interpolation, quality evaluation, and feature engineering.
The trained models are deployed to predict short-term damage events that may lead to immediate failure, such as broken shaft, short-circuit, grounded downhole sensor failure, as well as long-term events that build up over time, such as sand and scale deposition.
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FORESIGHT’s Physics-driven, rule-based ML approach detects imminent problems before they occur, helping to drastically reduce downtime losses per well.
Live data integration and real-time data overlaid with AI modeling is utilized to recommend actions for preventing ESP trip/failure, raising alarms as soon as failures are detected, reducing the possibility of further damage.
Digital twin capabilities are implemented, creating an optimal combination of Physics- and data-based models.
Leverages AI, live data analysis, Physics, and knowledge-based methods to predict electrical and mechanical events.
Live data integration and real-time data overlaid with AI modeling is utilized to recommend actions.
Digital twin capabilities are implemented, creating an optimal combination of Physics- and data-based models.
Reliable prediction of ESP failures to meet common ESP-related operating challenges.
Reducing production loss due to ESP downtime (minimized differed production).
Increased productivity by providing more time to business planning and decision-making.
Better planning facilitated for the allocation of rigs.
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