Failure Mode Identification of Elastomer for Well Completion Systems using Mask R-CNN

Weihang Chen, Li Zhang, Ming Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Sealing mechanisms are at the heart of any drilling, completion, or production system and are the primary components on which the functional success and longevity of the system rest. Element extrusion and cracking are the earliest failure modes to indicate leakage on the elastomers. With the trend towards higher operating temperatures and pressures of the well completion systems, more accurate and reliable methods are needed to identify the failure modes of the elastomer. Currently, elastomer failure type tests are carried out by a trained human supervisor. This method is time consuming and inefficient in inspecting a large quantity of elastomers. Furthermore, this manual approach depends on the human examiner’s knowledge and experience, which is subjective and may be prone to errors. To overcome these issues, we employ Mask R-CNN with a modified loss function to identify the failure types and localize failure regions of elastomers of the well completion systems. To train Mask R CNN for crack and extrusion detection, a total of 70 elastomer images with different types of failure conditions are collected from real-world environments by the oil and gas company. The VGG annotator is used to annotate the labels of the elastomer failure types and positions with polygonal regions in the images. Diverse image augmentation techniques are also used to further enhance performance. The mean average precision results are used to indicate model performance with different datasets. The empirical results indicate the robustness and efficiency of Mask R-CNN in performing failure detection and segmentation of elastomers.
Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
Number of pages8
ISBN (Electronic)978-1-7281-8671-9
ISBN (Print)978-1-6654-9526-4
DOIs
Publication statusPublished - 30 Sept 2022

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