Methods and systems for operational surveillance of a physical asset using smart event detection

A system for monitoring operation or status of a physical asset at a wellsite, comprising:

• at least one processor configured to perform operations that involve;
• i) receiving or collecting time-series data related to operation or status of the physical asset;
• ii) identifying a time period when the physical asset is experiencing a change in operational state, wherein the time period is identified by a first user with a first graphical user interface that displays the time-series data of i);
• iii) extracting time-series data corresponding to the time period of ii) as event data;
• iv) generating label data that classifies or characterizes the event data of iii) as pertaining to a particular type of event of a plurality of types of events;
• v) identifying one or more events of interest based on the plurality of types of events, wherein the one or more events of interest are identified by the first user with the first graphical user interface;
• vi) saving the event data of iii) and the corresponding label data of iv) in a data repository;
• vii) using the event data and label data stored in the data repository in vi) to train or update a machine learning system to detect the occurrence of events that are similar to the event types of the labeled event data stored in the data repository from time-series data generated by the physical asset or by another physical asset that operates in a similar manner to the physical asset;
• viii) issuing a notification to a second user, via a second graphical interface, regarding the one or more events of interest;
• and
• ix) issuing a command to control the physical asset based on the one or more events of interest.

A non-transitory, computer-readable medium containing instructions for monitoring operation or status of a physical asset at a wellsite, the instructions causing a processor to execute operations comprising:

• i) receiving or collecting time-series data related to operation or status of the physical asset;
• ii) identifying a time period when the physical asset is experiencing a change in operational state, wherein the time period is identified by a first user with a first graphical user interface that displays the time-series data of i);
• iii) extracting time-series data corresponding to the time period of ii) as event data;
• iv) generating label data that classifies or characterizes the event data of iii) as pertaining to a particular type of event of a plurality of types of events;
• v) identifying one or more new types of events to generate a new plurality of types of events, wherein the one or more new types of events are identified by the first user with the first graphical user interface;
• vi) identifying one or more events of interest based on the plurality of types of events, wherein the one or more events of interest are identified by the first user with the first graphical user interface;
• vii) saving the event data of iii) and the corresponding label data of iv) in a data repository;
• viii) using the event data and label data stored in the data repository in vii) to train or update a machine learning system to detect the occurrence of events that are similar to the event types of the labeled event data stored in the data repository from time-series data generated by the physical asset or by another physical asset that operates in a similar manner to the physical asset;
• ix) issuing a notification to a second user, via a second graphical interface, regarding the one or more events of interest;
• and
• x) issuing a command to control the physical asset based on the one or more events of interest.