Simple Applications of Machine Learning in Subsurface Characterization

Dr. Misra will present few case studies on the use of machine learning techniques. In the first case study, neural network models generate NMR T2 distribution in the absence of NMR logging tool. In the second case study, simple data-driven models generate compressional and shear travel time logs in the absence of sonic logging tool. In the third case study, machine learning assisted the segmentation of SEM images of shale samples. This segmentation method involves two steps, feature extraction from SEM images followed by random forest classification of each pixel in the SEM image. In the fourth case study, machine learning was used to process CT scan images to predict the subsurface geomechanical properties.