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The robustness of oil and gas wells depends on the condition of their casings. Every five to six years, such casings undergo major repairs, during which equipment gets disassembled and test loads are performed. In addition, oilfield operators conduct regular monitoring of casings, which requires no production shutdowns.

One of the most popular methods is to monitor the so-called magnetic flux leakage, which is applicable to casings made of magnetisable metals (e.g. iron and nickel): a device is placed in a wellbore casing to study the magnetic field and measure the distortions produced by the existing defects. Monitoring is conducted with the use of databases concerning typical defects in pipes made of various materials. Neural networks, which are used to inspect a specific pipe casing identify the signs of a typical defect and assess its parameters. However, each real defect has a unique shape, which is why the results of such inspections are often rather imprecise.

“The traditional approach is unsuccessful in cases when defects do not conform to the assumed shape and when the properties of the laboratory casing are not the same as the properties of the installed casing,” Denis Goldobin, one of the authors of the study and candidate of physical and mathematical sciences, is quoted as saying by the Perm Federal Research Center of UB RAS. The project participants have devised a new approach that requires no background knowledge about the casing material – all necessary information comes from device measurements. “After that, our mathematical model makes it possible to calculate the thickness profile of the casing,” Denis Goldobin concludes. Device measurements are shown in digital form as a defect profile that allows the operator to decide if urgent repairs are needed or if the existing deformations do not interfere with the safe operation of the wellbore casing.