Title:
Localization of Thermal Wellbore Defects Using Machine Learning
Localization of Thermal Wellbore Defects Using Machine Learning
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Bruss, Kathryn J.
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Mazumdar, Anirban
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Abstract
Defect detection and localization are key to preventing environmentally damaging wellbore leakages in both geothermal and oil/gas applications. In this work, a multi-step, machine learning approach is used to localize two types of thermal defects within a wellbore model. This approach includes a COMSOL heat transfer simulation to generate base data, a neural network to classify defect orientations, and a localization algorithm to synthesize sensor estimations into a predicted location. A physical test bed was created to verify the approach using experimental data. The test bed is a small-scale wellbore model. The classification and localization results were quantified using this experimental data. The classification predicted all experimental defect orientations correctly. The localization algorithm predicted the defect location with an average root mean square error of 1.837 in. The core contributions of this work are 1) the overall localization architecture, 2) the use of centroid-guided mean-shift clustering for localization, 3) the experimental validation and quantification of performance.
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2020-11-24
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