Invention Grant
- Patent Title: Deep learning methods for wellbore leak detection
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Application No.: US17114753Application Date: 2020-12-08
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Publication No.: US11353617B1Publication Date: 2022-06-07
- 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
- Main IPC: G01V1/50
- IPC: G01V1/50 ; E21B47/002 ; E21B47/107 ; G01M3/24

Abstract:
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.
Public/Granted literature
- US20220179117A1 DEEP LEARNING METHODS FOR WELLBORE LEAK DETECTION Public/Granted day:2022-06-09
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