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Wall thickness image construction in wellbore casings using data-driven inversion of magnetic flux leakage

Said Assous, Anastasiya V. Dolmatova, Denis S. Goldobin, Peter Elkington and Mark Bacciarelli
Wall thickness image construction in wellbore casings using data-driven inversion of magnetic flux leakage
Geophysics (December 2022) 87 (6): D175-D187

Abstract

We describe the response of magnetic flux leakage measurements obtained in wellbore casings and detail an analytical approach for inverting the data to construct a 3D image of metal loss for the general case in which the magnetic permeability of the casing is unknown. This approach contrasts with current practice which assumes a defect shape and attempts to deduce metal loss from one or more response metrics matched to a library of defects generated under laboratory conditions for that shape. The traditional approach breaks down where 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. Moreover, there is no direct way to know whether magnetic flux in the casing is at saturation level; thus, the measurement could be operating in a nonlinear part of the magnetization curve whose shape also is unknown. The new method makes no assumptions about defect shape and approximates the relevant part of the magnetization curve within an inversion process that transforms the flux scan into a metal loss scan showing the shape and depth of metal loss. The approach is validated using data from simulations, laboratory tests, and field operations. Unlike images of flux distribution, thickness profiles reconstructed from the results of the inversion reflect the actual distribution of metal loss to a useful level of accuracy and thereby enable the casing's mechanical integrity to be assessed with greater confidence.


ISSN: 0016-8033
EISSN: 1942-2156
Coden: GPYSA7
Serial Title: Geophysics
Serial Volume: 87
Serial Issue: 6
Title: Wall thickness image construction in wellbore casings using data-driven inversion of magnetic flux leakage
Affiliation: Data Science & Sensor Physics, Loughborough, United Kingdom
Pages: D175-D187
Published: 202212
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 39
Accession Number: 2022-061780
Categories: Applied geophysicsEngineering geology
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus.
Secondary Affiliation: Russian Academy of Sciences, Institute of Continuous Media Mechanics, Perm, RUS, Russian Federation
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2022, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 202221
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