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1.发明授权公开(公告)号：US11003952B2
公开(公告)日：2021-05-11
申请号：US16506891
申请日：2019-07-09
发明人： Zhou Feng , Hongliang Wu , Ning Li , Kewen Wang , Peng Liu , Yusheng Li , Huafeng Wang , Binsen Xu
摘要： A method and an apparatus for automatically recognizing an electrical imaging well logging facies, wherein the method comprises: acquiring historical data of electrical imaging well logging; pre-processing the historical data of the electrical imaging well logging to generate an electrical imaging well logging image covering a full hole; recognizing a typical imaging well logging facies in the electrical imaging well logging image covering the full hole, and determining the electrical imaging well logging image covering the full hole as a training sample in accordance with a category of the imaging well logging facies; constructing a deep learning model including an input layer, a plurality of hidden layers, and an output layer; training the deep learning model using the training sample to obtain a trained deep learning model; and recognizing the well logging facies of the electrical imaging well logging image of the well section to be recognized using the trained deep learning model.
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公开(公告)号：US11010629B2
公开(公告)日：2021-05-18
申请号：US16507270
申请日：2019-07-10
发明人： Zhou Feng , Ning Li , Hongliang Wu , Kewen Wang , Peng Liu , Yusheng Li , Huafeng Wang , Chen Wang
摘要： A method and an apparatus for automatically extracting image features of electrical imaging well logging, wherein the method comprises the steps of: acquiring historical data of electrical imaging well logging; pre-processing the historical data of the electrical imaging well logging to generate an electrical imaging well logging image covering a full hole; recognizing and marking a typical geological feature in the electrical imaging well logging image covering the full hole, obtaining a processed image, and determining the processed image as a training sample according to types of the geological features; constructing a deep learning model including an input layer, a plurality of hidden layers, and an output layer; training the deep learning model using the training sample; using the trained deep learning model, recognizing type of a geological feature of an electrical imaging well logging image of a well section to be recognized, and performing morphological optimization processing on the recognition result to obtain a feature optimization recognition result. The solution can automatically, quickly and accurately recognize the typical geological features in the electrical imaging well logging image.
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3.发明申请公开(公告)号：US20200065620A1
公开(公告)日：2020-02-27
申请号：US16506891
申请日：2019-07-09
发明人： Zhou Feng , Hongliang Wu , Ning Li , Kewen Wang , Peng Liu , Yusheng Li , Huafeng Wang , Binsen Xu
摘要： A method and an apparatus for automatically recognizing an electrical imaging well logging facies, wherein the method comprises: acquiring historical data of electrical imaging well logging; pre-processing the historical data of the electrical imaging well logging to generate an electrical imaging well logging image covering a full hole; recognizing a typical imaging well logging facies in the electrical imaging well logging image covering the full hole, and determining the electrical imaging well logging image covering the full hole as a training sample in accordance with a category of the imaging well logging facies; constructing a deep learning model including an input layer, a plurality of hidden layers, and an output layer; training the deep learning model using the training sample to obtain a trained deep learning model; and recognizing the well logging facies of the electrical imaging well logging image of the well section to be recognized using the trained deep learning model.
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公开(公告)号：US11086034B2
公开(公告)日：2021-08-10
申请号：US16159052
申请日：2018-10-12
发明人： Ning Li , Hongliang Wu , Zhou Feng , Peng Liu , Kewen Wang , Qingfu Feng , Yusheng Li
IPC分类号： G01V1/30 , G01V3/20 , G01V11/00 , E21B49/00 , G01V1/50 , G16Z99/00 , G01N15/08 , G01V3/38 , G01V5/04
摘要： The embodiments of the present disclosure disclose a method and an apparatus for determining the permeability of the reservoir. The method comprises: acquiring logging data corresponding to the two zones at least; determining the permeability of a specified zone in the two zones at least based on logging data corresponding to the specified zone, wherein the specified zone represents any one of the two zones at least; setting weight values corresponding to the at least two zones respectively; and determining the permeability of the reservoir based on the weight values and the permeability respectively corresponding to the two zones at least. The technical solutions provided by the embodiments of the present disclosure can improve the accuracy of the determined permeability of the reservoir.
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5.发明申请公开(公告)号：US20200065606A1
公开(公告)日：2020-02-27
申请号：US16507270
申请日：2019-07-10
发明人： Zhou Feng , Ning Li , Hongliang Wu , Kewen Wang , Peng Liu , Yusheng Li , Huafeng Wang , Chen Wang
摘要： A method and an apparatus for automatically extracting image features of electrical imaging well logging, wherein the method comprises the steps of: acquiring historical data of electrical imaging well logging; pre-processing the historical data of the electrical imaging well logging to generate an electrical imaging well logging image covering a full hole; recognizing and marking a typical geological feature in the electrical imaging well logging image covering the full hole, obtaining a processed image, and determining the processed image as a training sample according to types of the geological features; constructing a deep learning model including an input layer, a plurality of hidden layers, and an output layer; training the deep learning model using the training sample; using the trained deep learning model, recognizing type of a geological feature of an electrical imaging well logging image of a well section to be recognized, and performing morphological optimization processing on the recognition result to obtain a feature optimization recognition result. The solution can automatically, quickly and accurately recognize the typical geological features in the electrical imaging well logging image.
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