MACHINE LEARNING FOR AUTOMATIC CASING ANOMALY CLASSIFICATION FROM ELECTROMAGNETIC DATA

1 . A computer-implemented method comprising:

accessing a first database holding results of interpreting casing integrity, wherein each result provides a label for a detected anomaly at a depth location of an inspection log, wherein the label is one of: a first label of actual metal loss, or a second label of anomaly due to other factors, and wherein the inspection log records electromagnetic (EM) survey data of an underground metal casing that runs a plurality of depth locations;

accessing a second database holding inspection logs, wherein each inspection log record EM survey data of a corresponding underground metal casing that runs the plurality of depth locations;

based on, at least in part, the results of interpreting casing integrity, training a deep learning model configured to classify an input inspection log comprising an anomaly into the first label or the second label;

applying the deep learning model to one or more unclassified inspection logs of the second database, wherein the one or more unclassified inspection logs of the second database comprising anomalies; and

subsequently classifying the one or more unclassified inspection logs of the second database into either the first label or the second label.

2 . The computer-implemented method of claim 1 , further comprising:

analyzing the one or more unclassified inspection logs of the second database such that the anomalies in the one or more unclassified inspection logs are detected.

3 . The computer-implemented method of claim 1 , wherein each result from the first database is generated based on, at least in part, a determination by one or more human experts when presented with the inspection log along with the anomaly.

4 . The computer-implemented method of claim 3 , wherein each result from the first database is generated in view of a schematic of the underground metal casing at the plurality of depth locations.

5 . The computer-implemented method of claim 4 , wherein the schematic of the underground metal casing reveals at least one of: an eccentric casing pipe configuration, a decentered casing pipe configuration, and a casing pipe size transition.

6 . The computer-implemented method of claim 1 , wherein said EM survey data comprise an EM spectrum map corresponding to recorded EM decay curves from each transmitter-receiver combination on an EM logging tool lowered into the underground metal casing.

7 . The computer-implemented method of claim 6 , wherein the first label is characterized by a trapezoid pattern in the EM spectrum map at a depth location corresponding to the detected anomaly.

8 . The computer-implemented method of claim 1 , wherein the deep learning model includes a U-Net classifier.

9 . The computer-implemented method of claim 8 , wherein the U-Net classifier comprises:

a first stage configured to perform a pixel-level classification and classify each pixel or each patch of pixels into either the first label or the second label, and

a second stage of using morphological patterns to discriminate detected anomalies according to a respective pattern of each detected anomaly.

10 . The computer-implemented method of claim 1 , wherein the deep learning model includes a patch-based image classifier configured to operate on patches of pixels,

wherein the patch-based image classifier incorporates a convolutional neural network (CNN) classifier, and

wherein the CNN classifier comprises:

classifying, as a classified label, patches of each inspection image into one of the first label or the second label:

assigning the classified label to a center pixel of the patch; and

averaging over adjacent patches.

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

accessing a first database holding results of interpreting casing integrity, wherein each result provides a label for a detected anomaly at a depth location of an inspection log, wherein the label is one of: a first label of actual metal loss, or a second label of anomaly due to other factors, and wherein the inspection log records electromagnetic (EM) survey data of an underground metal casing that runs a plurality of depth locations;

accessing a second database holding inspection logs, wherein each inspection log record EM survey data of a corresponding underground metal casing that runs the plurality of depth locations;

based on, at least in part, the results of interpreting casing integrity, training a deep learning model configured to classify an input inspection log comprising an anomaly into the first label or the second label;

applying the deep learning model to one or more unclassified inspection logs of the second database, wherein the one or more unclassified inspection logs of the second database comprising anomalies; and

subsequently classifying the one or more unclassified inspection logs of the second database into either the first label or the second label.

12 . The computer system of claim 11 , wherein the operations further comprise:

analyzing the one or more unclassified inspection logs of the second database such that the anomalies in the one or more unclassified inspection logs are detected.

13 . The computer system of claim 11 , wherein each result from the first database is generated based on, at least in part, a determination by one or more human experts when presented with the inspection log along with the anomaly.

14 . The computer system of claim 13 , wherein each result from the first database is generated in view of a schematic of the underground metal casing at the plurality of depth locations.

15 . The computer system of claim 14 , wherein the schematic of the underground metal casing reveals at least one of: an eccentric casing pipe configuration, a decentered casing pipe configuration, and a casing pipe size transition.

16 . The computer system of claim 11 of claim 11 , wherein said EM survey data comprise an EM spectrum map corresponding to recorded EM decay curves from each transmitter-receiver combination on an EM logging tool lowered into the underground metal casing.

17 . The computer system of claim 16 , wherein the first label is characterized by a trapezoid pattern in the EM spectrum map at a depth location corresponding to the detected anomaly.

18 . The computer system of claim 11 , wherein the deep learning model includes a U-Net classifier.

19 . The computer system of claim 18 , wherein the U-Net classifier comprises:

a first stage configured to perform a pixel-level classification and classify each pixel or each patch of pixels into either the first label or the second label, and

a second stage of using morphological patterns to discriminate detected anomalies according to a respective pattern of each detected anomaly.

20 . The computer system of claim 11 , wherein the deep learning model includes a patch-based image classifier configured to operate on patches of pixels,

wherein the patch-based image classifier incorporates a convolutional neural network (CNN) classifier, and

wherein the CNN classifier comprises:

classifying, as a classified label, patches of each inspection image into one of the first label or the second label;

assigning the classified label to a center pixel of the patch; and

averaging over adjacent patches.