Identification of wellbore defects using machine learning systems

A method for identifying defects, comprising:

• receiving log data, the log data corresponding to one or more wellbore operations, the log data including data from at least one measurement modality corresponding to a present measurement modality;
• training, using the log data, a machine learning model;
• acquiring wellbore data, via the present measurement modality, during a logging operation;
• processing at least a portion of the wellbore data using the trained machine learning model;
• and
• identifying one or more features of interest in the wellbore data, via the trained machine learning model.

A method for identifying defects in a wellbore, comprising:

• receiving log data, the log data corresponding to one or more wellbore operations;
• training, using the log data, a machine learning model;
• acquiring wellbore data, during a logging operation;
• normalizing at least a portion of the wellbore data, the normalized portion of the wellbore data corresponding to a location of interest;
• processing the normalized portion of wellbore data using the trained machine learning model;
• determining, via a first classifier of the machine learning model, whether a feature of interest is present in the normalized portion of the wellbore data;
• upon determining the feature of interest is not present, ending processing of the normalized portion of the wellbore data;
• and
• upon determining the feature of interest is present, determining one or more properties of the feature of interest using at least one additional classifier.