Abstract: Disclosed a method for predicting permeability of a multi-mineral phase digital core based on deep learning. In the present disclosure, a three-dimensional digital core is constructed and a pore structure is randomly generated; after a plurality of multi-mineral digital core images is acquired by performing image segmentation on the three-dimensional digital core, permeability corresponding to each of the multi-mineral digital core images is acquired by simulation using multi-physics field simulation software and a multi-mineral digital core data set is constructed based on the plurality of multi-mineral digital core images and the permeability corresponding to each of the multi-mineral digital core images; an SE-ResNet18 convolutional neural network is constructed and trained with the multi-mineral digital core data set; and an image of a multi-mineral core to be predicted is input into the trained SE-ResNet18 convolutional neural network to determine the permeability of the multi-mineral core.

Abstract: A method of detecting the dryness of wet steam of a once-through steam injection boiler, comprises: first, by detecting and/or calculating, obtaining volume flow of wet steam, corresponding volume flow of saturated liquid, and corresponding volume flow of saturated vapour; and then obtaining the dryness of wet steam of the once-through steam injection boiler, X, according to the following relationship equation: X = ; wherein are the volume flow of wet steam, the corresponding volume flow of saturated liquid, and the corresponding volume flow of saturated vapour respectively. A detecting apparatus for detecting dryness of wet steam of a once-through steam injection boiler, specifically comprises: a data detecting module 1, a data processing module 2, and a data displaying module 3, wherein the data processing module 2 connects the data detecting module 1 and the data displaying module 3.