Imaging and Visualization

Imaging techniques have seen profound usage in evaluating the microstructures of shales and coals. This coupled with machine learning, has helped in better visualization of pore structural features and their connectivity of unconventional hydrocarbon reservoirs. Most common 2D imaging techniques are optical microscopy, SEM and TEM. While 2D imaging can generally go for higher resolution, but it presents lower field of view. In transitioning from 2 to 3D imaging, FIB-SEM stands out, operating like SEM but with in-situ ion-milling for layer-by-layer scanning, enabling depth information up to a few millimetres. While 3D imaging helps in developing pore network model, its smaller scan volume limits its ability to quantify the heterogeneity and anisotropy in shale and coal. Consequently, application of image processing, employing filtering, thresholding, feature extraction and quantification, application of ML and automated segmentation is extremely critical for visualizing thermal and flow attributes of shale and coal. For multiscale imaging for coal and shale reservoir characterization, optical-electron correlative microscopy, has recently gained prominence. This technique involves initially identifying organic matter using an optical microscope and subsequently examining the same particle under SEM for a more detailed analysis of its microstructure. This chapter explores the strengths and limitations of multiscale, multidimensional characterization of pores network, organic and mineral matter in coal and shale.