1. Automatic Defect Recognition in
Corrosion Logging using Magnetic
Imaging Defectoscopy data
Rita Gaibadullina, Bulat Zagidullin, Vladimir Bochkarev
Kazan Federal University, Kazan, Russia

2. Objective: To automate the search for
corrosions in oil wells according to MID
data
batteries
short sensor
(120 mm)
long sensor
(320 mm)
memory
The Magnetic Imaging Defectoscope (MID)
1 10 100
0
500
1000
1500
2000
Time, ms
Response,mV
short sensor
long sensor
Responses of the short and long MID sensors.

3. Casing and tubing corrosions
Algorithm of automatic corrosion
recognition:
2. Construction of binary
maps according to Drift
panels
1. Construction of Drift
panels according to MID
data
3. Making a decision on
significant deviation on
binary maps

4. Summary
1. The automatic defect recognition algorithm based on maximum
likelihood criterion accurately separates the defects of the 1st and
2nd barriers;
2. When configuring the algorithm to search for small intervals of
corrosion (metal loss less than 10%), lots of false defects are
present, which complicates data processing. With such
configuration, the algorithm detects 89% of the first barrier corrosions
and 93% of the second barrier corrosions.
3. The algorithm is designed to find major defects (metal loss greater
than 10%). When setting the appropriate algorithm parameters, all
defects, including a small number of false defects, are detected.

5. Thank you!