Patent ID: 11954417
Assignee: CHINA UNIVERSITY OF PETROLEUM (EAST CHINA)
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 5:
6. The method for predicting the permeability of the multi-mineral phase digital core based on deep learning according to claim 1, wherein step 4 specifically comprises:
step 4.1, constructing the SE-ResNet18 convolutional neural network by combining an SE module with an ResNet18 network model, wherein
the SE-ResNet18 convolutional neural network is constructed by embedding the SE module in a double-layer residual module of the ResNet18 network model, the ResNet18 network model consists of one convolutional layer, eight double-layer residual modules, and a fully connected layer, with each of the double-layer residual modules consisting of two 3×3 convolutional layers and a skip connection;
step 4.2, setting a learning rate, a loss function, a precision value, and a number of trainings of the SE-ResNet18 convolutional neural network;
step 4.3, training the SE-ResNet18 convolutional neural network with sample data in the training set and calculating permeability according to the multi-mineral digital core images: inputting the sample data in the training set into the SE-ResNet18 convolutional neural network, acquiring calculated values of permeability corresponding to each of the multi-mineral digital core images by calculation through the SE-ResNet18 convolutional neural network, and comparing the calculated values of the permeability corresponding to each of the multi-mineral digital core images with the labels of the multi-mineral digital core images to calculate a loss function value until a number of iterative calculations reaches a preset number of trainings, and proceeding to step 4.4;
step 4.4, in a case that the loss function value is always less than a preset precision value during training, determining the learning rate of the SE-ResNet18 convolutional neural network and proceeding to step 4.5; otherwise, performing hyper-parametric optimization on the learning rate of the SE-ResNet18 convolutional neural network, after resetting the learning rate of the SE-ResNet18 convolutional neural network, returning to step 4.3, and continuing to train the SE-ResNet18 convolutional neural network with the sample data in the training set;
step 4.5, validating a training effect of the SE-ResNet18 convolutional neural network with the validation set: inputting sample data in the validation set into the SE-ResNet18 convolutional neural network, acquiring calculated values of permeability corresponding to each of the multi-mineral digital core images in the validation set by calculation through the SE-ResNet18 convolutional neural network, and comparing the calculated values of the permeability corresponding to each of the multi-mineral digital core images with the labels of the multi-mineral digital core images in the validation set to calculate a loss function value until a number of iterative calculations reaches a preset number of trainings, and proceeding to step 4.6;
step 4.6, in a case that the loss function value is always less than the preset precision value during validation, ending training the SE-ResNet18 convolutional neural network and proceeding to step 4.7; otherwise, performing hyper-parametric optimization on the learning rate of the SE-ResNet18 convolutional neural network, after resetting the learning rate of the SE-ResNet18 convolutional neural network, returning to step 4.3, and continuing to train the SE-ResNet18 convolutional neural network with the sample data in the training set; and
step 4.7, testing calculation accuracy of the SE-ResNet18 convolutional neural network with the test set: inputting sample data in the test set into the SE-ResNet18 convolutional neural network, acquiring calculated value of permeability corresponding to each of the multi-mineral digital core images in the test set by calculation through the SE-ResNet18 convolutional neural network, and comparing the calculated values of the permeability corresponding to each of the multi-mineral digital core images with the labels of the multi-mineral digital core images in the test set to determine an accuracy rate of the SE-ResNet18 convolutional neural network.