Abstract
Magnetic flux leakage measurements help identify the position, size and shape of corrosion-related defects in steel casings used to protect boreholes drilled into oil and gas reservoirs. Images constructed from magnetic flux leakage data contain patterns related to noise inherent in the method. We investigate the patterns and their scaling properties for the case of delta-correlated input noise, and consider the implications for the method's ability to resolve defects. The analytical evaluation of the noise-produced patterns is made possible by model reduction facilitated by large-scale approximation. With appropriate modification, the approach can be employed to analyze noise-produced patterns in other situations where the data of interest are not measured directly, but are related to the measured data by a complex linear transform involving integrations with respect to spatial coordinates.
Highlights
A common challenge in engineering is the need to infer properties of interest from indirect measurements
In this paper we suggest and implement an approach to comprehensively analyse noise-produced patterns in magnetic flux leakage (MFL)-reconstructed data for different device designs
The problem set-up corresponds to measurements made with modern devices designed for the MFL inspection of wellbore casings described for example in [7]
Summary
A common challenge in engineering is the need to infer properties of interest from indirect measurements. The direct measurement of the property of interest may be possible but expensive or MFL: Noise-Produced Patterns. In this paper we suggest and implement an approach to comprehensively analyse noise-produced patterns in MFL-reconstructed data for different device designs. The problem set-up corresponds to measurements made with modern devices designed for the MFL inspection of wellbore casings described for example in [7]. Our approach is based on the mathematical methodology developed in [8] within the framework of large-scale approximation, which is relevant for the case of corrosive damage. Such model reduction provides the opportunity for a comprehensive analysis whose results remain qualitatively valid for the general case beyond large-scale approximation. A similar approach can be employed for various data reconstruction tasks
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