Quantitative analysis and feature detection for scanning electron microscopy images using machine learning and image processing

Citation:

X. Tian, Daigle, H., and Jiang, H., “Quantitative analysis and feature detection for scanning electron microscopy images using machine learning and image processing,” Unconventional Resources Technology Conference. Society of Petroleum Engineers/Society of Exploration Geophysicists/American Association of Petroleum Geologists, Houston, TX, 2018.

Abstract:

Microfractures are important mechanical discontinuities in shales and are important for fluid flow during production. Understanding their properties is crucial for accurate shale production prediction and implementing effective stimulation strategies. Scanning electron microscope (SEM) images are useful for characterizing shale microstructure, but manual image analysis is often challenging and time consuming. We present an alternative method for quickly characterizing microfractures and obtaining pore structure information from SEM images using machine learning algorithms and image processing. Using this approach, SEM images were obtained from deformed and intact samples of a carbonate rich shale and a siliceous shale with the goal of identifying microfractures. Support vector machine, convolutional neural networks, and four pretrained convolutional neural networks were implemented to differentiate SEM images containing fractures (frac-images) and SEM images containing no fractures (non-frac-images). Images containing fractures were identified with 92% training accuracy and 88% testing accuracy. A pretrained convolutional neural network with 16 layers (vgg16) was shown to perform best for this image classification task.

Notes:

Publisher's Version