Investigation of coal elastic properties based on digital core technology and finite element method

Organic shale is one of the most important unconventional resources all around the world. Total organic carbon (TOC) content is an important evaluation parameter of reservoir hydrocarbon source quality. The regular evaluation methods have higher requirements of well logs and core experiment data for statistical regression. Through analyzing the resistivity and gamma ray logging response characteristics of shale content and organic matters, combined with digital rock physics experiment simulation, we put forward and improve the dual-shale-content method for TOC content logging evaluation. The accuracy of this method is verified by actual data processing. The result shows the dual-shale-content method is simple to use and the error is small. And by comparing with the calculation results by using the Δ LogR method, it is revealed that the trend of the TOC content calculated by our method agrees with the core results better. This new method is suitable for the evaluation of TOC content in the area or interval where there is only conventional logs.

Identification and parameter characterization of pores and fractures in shales based on multi-scale digital core data

As the exploration of oil and gas moves further into less conventional reservoirs, effective methods are required for the fine evaluation of complex formations, particularly digital core models with multiple mineral components. The current technology cannot directly produce digital core images with multiple minerals. Therefore, image segmentation has been widely used to create digital multi-mineral core images from computed tomography (CT) images. The commonly used image segmentation methods do not provide satisfactory CT images of complex rock formations. Consequently, deep learning algorithms have been successfully applied for image segmentation. In this paper, a novel method is proposed to develop an accurate digital core model with multiple mineral components based on the Res-Unet neural network. CT images of glutenite and the corresponding results of quantitative evaluation of minerals by scanning electron microscopy are used as a training dataset for the automatic segmentation of CT core images. The used quantitative metrics show that compared with the multi-threshold and U-Net segmentation methods, the Res-Unet network leads to better results of mineral morphology and distribution recognition. Finally, it is demonstrated that the proposed Res-Unet-based segmentation model is an effective tool for creating three-dimensional digital core models with multiple mineral components.