公开(公告)号：US11010629B2

公开(公告)日：2021-05-18

申请号：US16507270

申请日：2019-07-10

申请人： PetroChina Company Limited

发明人： Zhou Feng ,  Ning Li ,  Hongliang Wu ,  Kewen Wang ,  Peng Liu ,  Yusheng Li ,  Huafeng Wang ,  Chen Wang

IPC分类号： G06K9/46 ,  G06T5/30 ,  G06N20/00 ,  G06N3/08 ,  G06T7/00

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.

公开(公告)号：US20200065606A1

公开(公告)日：2020-02-27

申请号：US16507270

申请日：2019-07-10

申请人： PetroChina Company Limited

发明人： Zhou Feng ,  Ning Li ,  Hongliang Wu ,  Kewen Wang ,  Peng Liu ,  Yusheng Li ,  Huafeng Wang ,  Chen Wang

IPC分类号： G06K9/46 ,  G06T5/30 ,  G06T7/00 ,  G06N3/08 ,  G06N20/00

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.