Detecting anomalies and predicting failures in petroleum-industry operations

A computer-implemented method for protecting resources associated with petroleum-industry operations, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising:

• monitoring data associated with resources associated with a petroleum-industry operation;
• detecting a system anomaly with an increased correlation to a system failure of the petroleum-industry operation, based on the monitored data, using machine learning;
• and
• performing a protective action to protect the resources associated with the petroleum-industry operation, based on detecting the system anomaly.

A computer-implemented method for protecting resources associated with petroleum-industry operations, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising:

• monitoring data associated with a plurality of resources associated with a petroleum-industry operation;
• detecting a system anomaly associated with the petroleum-industry operation based on the monitored data;
• determining a subset of the plurality of resources that correlate to the system anomaly;
• determining, for each of one or more resources of the subset of the plurality of resources, an estimated time to failure associated with the resource;
• determining a length of time until system failure of the petroleum-industry operation is estimated to occur, based on the estimated times to failure associated with the one or more resources;
• and
• performing a protective action to protect the plurality of resources associated with the petroleum-industry operation, based on determining the length of time until system failure of the petroleum-industry operation is estimated to occur.