Permeability prediction of considering organic matter distribution based on deep learning

At present, researchers predict permeability through core experiments that require specific experimental conditions and methods, which are difficult and time-consuming. Conventional simulation methods for predicting permeability require considerable computational resources. Therefore, deep learning can be used as a pore-scale simulation prediction method. In this study, we established a workflow for directly predicting permeability from images. Considering that the mineral properties of the nanopore wall of shale oil have a large influence on the flow, a core dataset with organic distribution was constructed with random circles, and the slip influence of organic pores was considered. From our dataset, we found that the average permeability with organic distribution was 32.3% higher than that without organic distribution. Therefore, to simulate the microscopic flow and predict the permeability of shale oil, considering the differences in the pore flow mechanisms of different minerals is necessary. We designed a convolutional network for the dataset, adopted the structure of SE-ResNet, added the squeeze-and-excitation (SE) module to the double-layer residual module of ResNet18, and combined the characteristics of the SE block with the attention mechanism and ResNet to effectively obtain the information between channels and avoid the problem of gradient disappearance or explosion. Using SE-ResNet for directly predicting the apparent permeability from images, the accuracy of the test set reached 88.5%. The model had strong generalization ability, and the SE-ResNet could map the image of the core to the apparent permeability, which was approximately 100 times faster than the direct flow simulation.

Automated summary

This study established a workflow for directly predicting permeability from images and found that the average permeability with organic distribution was 32.3% higher. A convolutional network model was designed using SE-ResNet structure and attention mechanism to achieve an accuracy of 88.5% in predicting permeability from images.