Deep learning methods for wellbore pipe inspection

1. A method for inspecting tubular integrity comprising:

conveying an electromagnetic pipe inspection tool inside an innermost tubular of multiple nested tubulars, wherein the electromagnetic pipe inspection tool has one or more transmitters and one or more receivers;

taking measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool;

arranging data from the measurements into a response image representative of a tool response to tubular integrity properties of the multiple nested tubulars, without requiring inversion of the measurements;

feeding the response image to a pre-trained deep neural network (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 properties of each of the multiple nested tubulars; and

determining, from the processed image, the presence of metal loss or corrosion in one or more locations within the multiple nested tubulars.

2. The method of claim 1 , wherein taking measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool comprises

transmitting electromagnetic fields at one or more frequencies with the one or more transmitters; and

measuring at least one of a real-part, an imaginary-part, an absolute, an amplitude, and a phase of a received signal at the one or more frequencies with the one or more receivers.

3. The method of claim 2 , wherein the one or more receivers comprise multiple receivers, wherein the one or more frequencies comprise multiple frequencies,

wherein the response image comprises a two-dimensional (2D) representation of the tool response,

wherein the measurements for each receiver of the multiple receivers and each frequency of the multiple frequencies form a log, and

wherein logs from the multiple receivers and the multiple frequencies are juxtaposed to form a 2D response image.

4. The method of claim 1 , wherein the electromagnetic pipe inspection tool is a time domain eddy current tool, wherein taking measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool comprises

exciting the multiple nested tubulars with pulsed electromagnetic fields with the one or more transmitters; and

measuring a decay response of the pulsed electromagnetic fields in the time domain with the one or more receivers.

5. The method of claim 4 , wherein the one or more receivers comprise multiple receivers, wherein the decay response measured by the multiple receivers comprises multiple time samples with different time delays,

wherein the response image comprises a 2D representation of the tool response,

wherein the measurements for each receiver of the multiple receivers and each time sample of the multiple time samples form a log, and

wherein logs from the multiple receivers and the multiple time samples are juxtaposed to form a 2D response image.

6. The method of claim 1 , wherein arranging the measurements into a response image comprises

mapping each measurement at each depth to a log data point on a log for each receiver; and

assigning a value to each pixel in the response image, wherein the value assigned is proportional to a percentage change of each log data point from a nominal value of that log data point.

7. The method of claim 1 , wherein the tubular integrity property comprises a cross-sectional thickness, a magnetic permeability, an electrical conductivity, or a combination thereof.

8. The method of claim 1 , wherein a value assigned to each pixel in the processed image is proportional to a percentage change of the tubular integrity property of each of the individual tubulars of the multiple nested tubulars from a nominal tubular integrity property of each of the individual tubulars of the multiple nested tubulars.

9. The method of claim 1 , wherein feeding the response image to the pre-trained DNN comprises splitting the response image into sections based on depth.

10. The method of claim 1 , wherein the pre-trained DNN further comprises at least one of a concatenation layer, a summation layer, a max pooling layer, an up-sampling layer, and a dense layer.

11. The method of claim 1 , further comprising training the DNN to provide the pre-trained DNN, wherein training the DNN comprises building a database by using at least one of measurements of known cases and simulation,

wherein the database includes a plurality of samples, and

wherein each sample of the plurality of samples comprises a true image of the tubular integrity property of each of the individual tubulars of the multiple nested tubulars and a corresponding response image.

12. The method of claim 11 , wherein training the DNN further comprises finding optimum network parameters to minimize a misfit between output images produced by the DNN and corresponding true images according to an error metric.

13. The method of claim 1 , wherein taking measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool comprises taking non-azimuthal, omnidirectional measurements of the multiple nested tubulars using the electromagnetic pipe inspection tool, wherein the image represents a variable density log of multiple data measurements.

14. The method of claim 1 , wherein taking measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool comprises taking azimuthal measurements of the multiple nested tubulars using the electromagnetic pipe inspection tool, wherein the response image comprises a three-dimensional (3D) representation of the tool response, and

wherein a first dimension is depth, a second dimension is azimuth, and a third dimension is a juxtaposition of measurements from multiple receivers and one of multiple frequencies and multiple time samples of the decay response at a given depth point and angular direction.

15. The method of claim 13 , wherein the processed image comprises a 3D representation of the tubular integrity property of each the individual tubulars of the multiple nested tubulars.

16. The method of claim 13 , wherein the convolutional layer comprises a convolutional filter, and wherein the convolutional filter is 3D filter.

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

instructions to initiate measurements of multiple nested tubulars with an electromagnetic pipe inspection tool conveyed inside an innermost tubular of the multiple nested tubulars;

instructions to arrange data from the measurements into a response image representative of a tool response to tubular integrity properties of the multiple nested tubulars, without requiring inversion of the measurements;

instructions to 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 properties of each of the multiple nested tubulars; and

instructions to determine, from the processed image, the presence of metal loss or corrosion in one or more locations within the multiple nested tubulars.

18. The computer-readable media of claim 17 , wherein the tubular integrity property comprises a cross-sectional thickness, a magnetic permeability, an electrical conductivity, or a combination thereof.

19. The computer-readable media of claim 17 , wherein a value assigned to each pixel in the processed image is proportional to a percentage change of the tubular integrity property of each of the multiple nested tubulars from a nominal tubular integrity property of each of the multiple nested tubulars.

20. 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 data from the measurements into a response image representative of a tool response to tubular integrity properties of the multiple nested tubulars, without requiring inversion of the measurements;

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; and

determine, from the processed image, the presence of metal loss or corrosion in one or more locations within the multiple nested tubulars.