Method and apparatus for automatically recognizing electrical imaging well logging facies

Patent metadata

Field	Value
Publication number	US-11003952-B2
Application number	US-201916506891-A
Country	US
Kind code	B2
Filing date	Jul 9, 2019
Priority date	Aug 24, 2018
Publication date	May 11, 2021
Grant date	May 11, 2021

Abstract

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.

First claim

The invention claimed is:

1. A method for automatically recognizing an electrical imaging well logging facies comprising:
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;
acquiring the electrical imaging well logging image of a well section; and
using the trained deep learning model, the electrical imaging well logging image of the well section to be identified is used for logging facies recognition;
the deep learning model is constructed as follows:
the deep learning model includes a seventeen-layer structure comprising: a first layer, a second layer, a third layer, a fourth layer, a fifth layer, a sixth layer, a seventh layer, an eighth layer, a ninth layer, a tenth layer, an eleventh layer, a twelfth layer, a thirteenth layer, a fourteenth layer, a fifteenth layer, a sixteenth layer, and a seventeenth layer;
a structure of each layer of the seventeen-layer structure is as follows:
the first layer is an input layer;
the second layer is a hidden layer, including a convolution layer, and an activation function layer;
the third layer is a hidden layer, including a convolution layer, an activation function layer, and a cooling layer;
the fourth layer is a hidden layer, including a convolution layer and an activation function layer;
the fifth layer is a hidden layer, including a convolution layer and an activation function layer, and a pooling layer;
the sixth layer is a hidden layer, including a convolution layer, and an activation function layer;
the seventh layer is a hidden layer, including a convolution layer, and an activation function layer;
the eighth layer is a hidden layer, including a convolution layer, an activation function layer, and a pooling layer;
the ninth layer is a hidden layer, including a convolution layer, and an activation function layer;
the tenth layer is a hidden layer, including a convolution layer, and an activation function layer;
the eleventh layer is a hidden layer, including a convolution layer, an activation function layer, and a pooling layer;
the twelfth layer is a hidden layer, including a convolution layer, and an activation function layer;
the thirteenth layer is a hidden layer, including a convolution layer, and an activation function layer;
the fourteenth layer is a hidden layer, including a convolution layer, an activation function layer, and a pooling layer;
the fifteenth layer is a hidden layer, including a full connection layer, an activation function layer and a Dropout layer;
the sixteenth layer is a hidden layer, including a full connection layer an activation function layer and a Dropout layer; and
the seventeenth layer is an output layer, including a full connection layer.

2. The method for automatically recognizing an electrical imaging well logging facies according to claim 1 , wherein pre-processing the historical data of the electrical imaging well logging to generate an electrical imaging well logging image covering the full hole includes:
performing acceleration correction processing and equalization processing on the historical data of the electrical imaging well logging to obtain an original electrical imaging weln logging image;
performing resistivity scale processing on the original electrical imaging well logging image to obtain a scale image reflecting resistivity of rocks of borehole wall formation; and
performing full hole image generation processing on the scale image reflecting resistivity of rocks of borehole wall formation, to generate an electrical imaging well logging image covering the full hole.

3. The method for automatically recognizing an electrical imaging well logging facies according to claim 2 , wherein using the trained deep learning model, the electrical imaging well logging image of the well section to be identified is used for logging facies recognition includes:
performing acceleration correction processing and equalization processing, resistivity scale processing, and a full hole image generation processing on the electrical imaging well logging image of the well section to be recognized, to generate an electrical imaging well logging image covering the full hole that is to be recognized;
performing depth window-by-depth window processing on the electrical imaging well logging image covering the full hole that is to be recognized, to obtain a plurality of depth window images; and
inputting the plurality of depth window images into the trained deep learning model to obtain a recognition result.

4. A computer device comprising:
a memory,
a processor, and
a computer program stored on the memory and executable on the processor, wherein when executing the computer program, the processor implementing the following:
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;
acquiring the electrical imaging well logging image of a well section; and
using the trained deep learning model, the electrical imaging well logging image of the well section to be identified is used for logging facies recognition; and
the deep learning model is constructed as follows:
the deep learning model includes a seventeen-layer structure comprising: a first layer, a second layer, a third layer, a fourth layer, a fifth layer, a sixth layer, a seventh layer, an eighth layer, a ninth layer, a tenth layer, an eleventh layer, a twelfth layer, a thirteenth layer, a fourteenth layer, a fifteenth layer, a sixteenth layer, and a seventeenth layer;
a structure of each layer of the seventeen-layer structure is as follows:
the first layer is an input layer;
the second layer is a hidden layer, including a convolution layer, and an activation function layer;
the third layer is a hidden layer, including a convolution layer, an activation function layer, and a pooling layer;
the fourth layer is a hidden layer, including a convolution layer and an activation function layer;
the fifth layer is a hidden layer, including a convolution layer and an activation function layer, and a pooling layer;
the sixth layer is a hidden layer, including a convolution layer, and an activation function layer;
the seventh layer is a hidden layer, including a convolution layer, and an activation function layer;
the eighth layer is a hidden layer, including a convolution layer, an activation function layer, and a cooling layer;
the ninth layer is a hidden layer, including a convolution layer, and an activation function layer;
the tenth layer is a hidden layer, including a convolution layer, and an activation function layer;
the eleventh layer is a hidden layer,

Assignees

• Petrochina Co Ltd

Inventors

• Feng Zhou
• Wu Hongliang
• Li Ning
• Wang Kewen
• Liu Peng
• Li Yusheng
• Wang Huafeng
• Xu Binsen

Classifications

• G06V10/82

  using neural networks · CPC title

• G06V10/454

  Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN] · CPC title

• G06N3/082 Primary

  modifying the architecture, e.g. adding, deleting or silencing nodes or connections · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06N3/048

  Activation functions · CPC title