Schneider Electric Systems Usa, Inc.

Fahd Saghir

Bartosz Boguslawski

Matthieu Boujonnier

Loryne Bissuel-beauvais

Reynaldo Espana Rey

Giorgio Colangelo

Jacky Hoareau

Xavier Pasbeau

Loryne Bissuel Beauvais

Helenio Gilabert

Systems and methods for real-time monitoring and control of well operations at a well site use machine learning (ML) based analytics at the well site. The systems and methods perform ML-based analytics on data from the well site via an edge device directly at the well site to detect operations that fall outside expected norms and automatically respond to such abnormal operations. The edge device can issue alerts regarding the abnormal operations and take predefined steps to reduce potential damage resulting from such abnormal operations. The edge device can also anticipate failures and a time to failure by performing ML-based analytics on operations data from the well site using normal operations data. This can help decrease downtime and minimize lost productivity and cost as well as reduce health and safety risks for field personnel.

Predicting well site failure modes and times using machine learning analytics and dynacard classifications

Well site machine learning based analytics

Systems and methods for real-time monitoring and control of well site operations employ well site edge analytics to detect abnormal operations. The systems and methods use machine learning (ML) based analytics on an edge device directly at the well site to detect possible occurrence of abnormal events and automatically respond to such events. The event detection may be based on trends identified in the data acquired from the well site operations in real time. The trends may be identified by correlation and by fitting line segments to the data and analyzing the slopes of the line segments. Upon detecting an unusual event, the edge device can issue alerts regarding the event and take predefined steps to reduce potential damage resulting from such event. This can help decrease downtime and minimize lost productivity and cost as well as reduce health and safety risks for field personnel.

Autonomous failure prediction and pump control for well optimization

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Fahd Saghir