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;
• perform an inversion on the measurements for a tubular integrity property of each individual tubular of the multiple nested tubulars to provide inverted tubular integrity properties;
• arrange the inverted tubular integrity property into an inverted image representative of an estimated tubular integrity property of each individual tubular;
• and
• feed the inverted image to a pre-trained DNN to produce a corrected image,
• wherein the DNN comprises at least one convolutional layer, and
• wherein the corrected image comprises a representation of a corrected 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;
• perform an inversion on the measurements for a tubular integrity property of each individual tubular of the multiple nested tubulars to provide inverted tubular integrity properties;
• arrange the inverted tubular integrity properties into an inverted image representative of an estimated tubular integrity property of each individual tubular;
• and
• feed the inverted image to the pre-trained DNN to produce a corrected image,
• wherein the corrected image comprises a representation of a corrected tubular integrity property of each individual tubular of the multiple nested tubulars.