Invention Grant
- Patent Title: Deep learning methods for wellbore pipe inspection
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Application No.: US17114591Application Date: 2020-12-08
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Publication No.: US11976546B2Publication Date: 2024-05-07
- Inventor: Ahmed Elsayed Fouda , Junwen Dai , Li Pan
- Applicant: Halliburton Energy Services, Inc.
- Applicant Address: US TX Houston
- Assignee: Halliburton Energy Services, Inc.
- Current Assignee: Halliburton Energy Services, Inc.
- Current Assignee Address: US TX Houston
- Agency: DeLizio, Peacock, Lewin & Guerra, LLP
- Main IPC: E21B47/002
- IPC: E21B47/002

Abstract:
Methods and systems for inspecting the integrity of multiple nested tubulars are included herein. A method for inspecting the integrity of multiple nested tubulars can comprise conveying an electromagnetic pipe inspection tool inside the innermost tubular of the multiple nested tubulars; taking measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool; arranging the measurements into a response image representative of the tool response to the tubular integrity properties of the multiple nested tubulars; and feeding the response image to a pre-trained deep neural network (DNN) to produce a processed image, wherein the DNN comprises at least one convolutional layer, and wherein the processed image comprises a representation of the tubular integrity property of each individual tubular of the multiple nested tubulars.
Public/Granted literature
- US20220178244A1 DEEP LEARNING METHODS FOR WELLBORE PIPE INSPECTION Public/Granted day:2022-06-09
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