- 专利标题: Deep learning methods for wellbore leak detection
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申请号: US17114753申请日: 2020-12-08
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公开(公告)号: US11353617B1公开(公告)日: 2022-06-07
- 发明人: Ahmed Elsayed Fouda , Junwen Dai , Li Pan
- 申请人: Halliburton Energy Services, Inc.
- 申请人地址: US TX Houston
- 专利权人: Halliburton Energy Services, Inc.
- 当前专利权人: Halliburton Energy Services, Inc.
- 当前专利权人地址: US TX Houston
- 代理机构: Delizio, Peacock, Lewin & Guerra
- 主分类号: G01V1/50
- IPC分类号: G01V1/50 ; E21B47/002 ; E21B47/107 ; G01M3/24
摘要:
Methods and systems for leak detection are provided herein. A method for leak detection can comprise conveying an acoustic leak detection tool inside the innermost tubular of the multiple nested tubulars; taking measurements of the multiple nested tubulars at multiple measurement depths with the acoustic leak detection tool; arranging the measurements into a response image; and feeding the response image to a pre-trained deep neural network (DNN) to produce a flow likelihood image, wherein the DNN comprises at least one convolutional layer, and wherein the flow likelihood image comprises a representation of one or more flow patterns in at least one annulus formed by the multiple nested tubulars.
公开/授权文献
- US20220179117A1 DEEP LEARNING METHODS FOR WELLBORE LEAK DETECTION 公开/授权日:2022-06-09
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