System and method using machine learning for automated casing and/or tubing inspection log interpretation

A computer system comprising one or more computer processors configured to perform operations of:

• accessing a first database holding information encoding a set of labels, wherein the set of labels specify a condition of at least one of:
• a surface pipe, or an underground enclosure, wherein the at least one underground enclosure includes a tubing or a casing, either of which runs at a plurality of depth locations;
• accessing a second database holding a plurality of inspection logs, wherein the plurality of inspection logs record measurement data of the at least one of the surface pipe or the underground enclosure that runs at the plurality of depth locations;
• based on, at least in part, the set of labels and the plurality of inspection logs, training a deep learning model configured to classify, into the set of labels, the condition of the at least one of the surface pipe or the underground enclosure that runs at the plurality of depth locations, when presented with the inspection logs;
• applying the deep learning model to one or more newly received inspection logs containing measurement data of a new surface pipe or a new underground enclosure;
• and
• subsequently classifying, into the set of labels, the condition of the new surface pipe or the new underground enclosure.