Method and apparatus for automatically recognizing electrical imaging well logging facies

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, 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.

5. The computer device according to claim 4 , wherein when executing the computer program, the processor implementing the following:

to generate the electrical imaging well logging image covering the full hole, pre-processing the historical data of the electrical imaging well logging by:

performing acceleration correction processing and equalization processing on the historical data of the electrical imaging well logging to obtain an original electrical imaging well 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.

6. The computer device according to claim 5 , wherein when executing the computer program, the processor implementing the following:

using the trained deep learning model, the electrical imaging well logging image of the well section to be identified is used for logging facie recognition, by:

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.

7. A non-transitory computer-readable medium, wherein the non-transitory computer-readable medium stores a computer program for executing the following method of:

acquiring historical data of electrical imaging well logging;

pw-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 computer program is used for executing the following:

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 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.

8. The non-transitory computer-readable medium according to claim 7 , wherein the computer program is used for executing the following:

to generate the electrical imaging well logging image covering the full hole, pre-processing the historical data of the electrical imaging well logging by:

performing acceleration correction processing and equalization processing on the historical data of the electrical imaging well logging to obtain an original electrical imaging well 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.

9. The non-transitory computer-readable medium according to claim 8 , wherein the computer program is used for executing the following:

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, by:

performing acceleration correction processing and equalization processing, resistivity scale processing, and 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.