Abstract: 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 receive well site data from a remote programmable automation (PAC) controller at the well site, the well site data representing one or more operational parameters related to the well site operations. A probability is derived for a given slope for each one of the one or more operational parameters as correlated to a different one of the one or more operational parameters to produce correlated probabilities for the one or more operational parameters. A resultant probability is derived from the correlated probabilities for the one or more operational parameters and it is determined whether the resultant probability meets a preselected threshold probability value. A responsive action is initiated if the resultant probability fails to meet the preselected threshold probability value.

Abstract: 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 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.