Accenture Global Solutions Limited

Susarla Aditya

Rabinarayan Mishra

Subrahmanyam Vadrevu

Jan Andre Nicholls

Jitender G Singh

Seshasai Rujuroop Kandrakota

Joel Titus

Sangameshwar Kardkal

Anusha K K

A device receives first well data associated with first wells where plunger lift is installed, and receives second well data associated with a candidate well where plunger lift is not installed. The device processes the first well data to determine pre-plunger lift installation data associated with the first wells, and processes the first well data and the pre-plunger lift installation data to determine a set of performing wells and a set of underperforming wells, of the first wells. The device processes the second well data and data associated with the set of performing wells to determine a first metric associated with the candidate well, and processes the second well data and data associated with the set of underperforming wells to determine a second metric associated with the candidate well. The device determines whether to install plunger lift in the candidate well based on the first metric and the second metric.

Utilizing analytical models to identify wells in which to install plunger lift for improved well production

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to analyze and categorize events associated with an equipment asset, such as industrial machinery, to determine a status (e.g., insight) associated with the equipment asset, and to determine maintenance actions to be performed with respect to the equipment asset to prevent, or reduce the likelihood or severity of, occurrence of a fault at the equipment asset. Machine learning (ML) models may be trained to categorize events that are detected based on operating characteristics data associated with the equipment asset, to determine a status of the equipment asset, and to recommend one or more maintenance actions (or other actions). Output that indicates the maintenance actions may be displayed to a user or used to automatically initiate performance of one or more of the maintenance actions.

Machine learning systems for automated event analysis and categorization, equipment status and maintenance action recommendation

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Bengaluru, India

Susarla Aditya