Deep learning methods for wellbore pipe inspection

One or more non-transitory computer-readable media comprising program code for inspecting tubular integrity, the program code to:

• initiate measurements of multiple nested tubulars with an electromagnetic pipe inspection tool conveyed inside an innermost tubular of the multiple nested tubulars;
• arrange the measurements into a response image representative of a tool response to tubular integrity properties of the multiple nested tubulars;
• and
• feed the response image to a pre-trained DNN to produce a processed image,
• wherein the DNN comprises at least one convolutional layer, and
• wherein the processed image comprises a representation of the tubular integrity property of each individual tubular of the multiple nested tubulars.

A system comprising:

• an electromagnetic pipe inspection tool disposed inside an innermost tubular of multiple nested tubulars;
• a pre-trained DNN comprising at least one convolutional layer;
• a processor;
• and
• a computer-readable medium having program code executable by the processor to:
• initiate measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool conveyed inside the innermost tubular;
• arrange the measurements into a response image representative of a tool response to tubular integrity properties of the multiple nested tubulars;
• and
• feed the response image to the pre-trained DNN to produce a processed image, wherein the processed image comprises a representation of the tubular integrity property of each individual tubular of the multiple nested tubulars.