- 专利标题: Computer-implemented method of using a non-transitory computer readable memory device with a pre programmed neural network and a trained neural network computer program product for obtaining a true borehole sigma and a true formation sigma
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申请号: US17476684申请日: 2021-09-16
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公开(公告)号: US11703611B2公开(公告)日: 2023-07-18
- 发明人: Sheng Zhan , Jeremy Zhang
- 申请人: China Petroleum & Chemical Corporation , Sinopec Tech Houston
- 申请人地址: CN TX Beijing
- 专利权人: China Petroleum & Chemical Corporation
- 当前专利权人: China Petroleum & Chemical Corporation
- 当前专利权人地址: CN Beijing
- 代理机构: Novick, Kim & Lee, PLLC
- 代理商 Allen Xue
- 主分类号: G01V5/14
- IPC分类号: G01V5/14 ; G01V5/10 ; G06F17/18 ; G01V5/12 ; G06N3/045
摘要:
A computer-implemented method that uses a preprogrammed neural network and a trained neural network computer program product to predict and then compared borehole and formation sigmas, when using a pulse neutron source and at least three dual-function radiation detectors. These dual-function radiation detectors are used for detecting both neutrons and gamma rays and further pre-programmed to distinguish between neutrons and gamma rays by using pulse shape discrimination techniques. The trained neural network computer program product can be used on above-surface systems, as well as below surface systems like borehole assemblies in logging-while-drilling systems. Once thermal neutron time-decay signals and capture gamma ray time-decay signals are measured by the at least three-dual function radiation detectors, a non-transitory computer readable memory device with the trained neural network computer program product is used to obtain a true borehole sigma and true formation sigma as the measurements are not affected by near-wellbore environments.
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