Digital rock core reconstruction method and system based on generative adversarial neural network

Abstract

The invention provides a digital rock core reconstruction method and system based on a generative adversarial neural network. The method comprises the following steps: executing iterative processing:processing historical noise data according to a weight matrix of a generator and bias of the generator to obtain a generated sample; processing the generated sample according to the weight of the discriminator and the bias of the discriminator to obtain a generated sample discrimination probability; processing the real sample according to the weight of the discriminator and the bias of the discriminator to obtain a real sample discrimination probability; determining a loss function according to the generated sample discrimination probability and the real sample discrimination probability; whenthe current number of iterations reaches a preset number of iterations; processing the current noise data according to the weight matrix of the generator and the bias of the generator in the currentiteration to obtain a digital rock core model; otherwise, updating the weight matrix of the generator, the bias of the generator, the weight of the discriminator and the bias of the discriminator according to the loss function, so that the reconstruction efficiency can be improved, and the digital rock core conforms to the distribution characteristics of the real rock core.

Description

technical field [0001] The present invention relates to the technical field of digital rock core reconstruction, in particular to a digital rock core reconstruction method and system based on a generative confrontational neural network. Background technique [0002] With the development of microscopic imaging technologies such as CT scanning in recent years, digital cores can more accurately describe the spatial distribution of various components in three-dimensional space. At the same time, digital cores themselves are also an important platform for studying microscopic seepage laws. In addition, based on the digital core model, the conductivity and acoustic properties of the core can also be studied. [0003] At present, the technology of reconstructing digital cores using generative adversarial neural networks is in its infancy. The process is complex, the reconstruction efficiency is low, and the connectivity of the obtained digital cores is poor, and there are errors wi...

AI Technical Summary

Problems solved by technology

[0003] At present, the technology of reconstructing digital rock cores by applying generative anti-neural networks is in its infancy. The process is complicated, the reconstruction efficiency is low, and the connectivity of the obtained digital rock cores is poor, and there are errors with real rock cores.

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