Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems: Prediction Models Exploiting Well-Log Information explores the implementation of machine and deep learning models to a range of subsurface geological prediction problems commonly encountered in applied resource evaluation and reservoir characterization tasks. It provides readers with insight into how the performance of ML/DL models can be optimized, and sparse datasets of input variables enhanced and/or rescaled, to improve their prediction performances. The author covers a variety of topics in detail, such as regression models to estimate total organic carbon from well-log data, predicting brittleness indexes in tight formation sequences, trapping mechanisms in potential sub-surface carbon storage reservoirs, and several more. Each chapter includes its own introduction, summary, and nomenclature sections together with one or more case studies focused on prediction model implementation related to its topic. The first part of each topic chapter describes the geological issues related to the topic, including an up-to-date literature review. The remainder focuses on prediction modeling of that topic including suitable machine learning and/or deep learning approaches and configurations. Case studies form the latter part of each chapter. Readers in this field will find an invaluable resource to assist them in applying machine and deep learning to their work in sub-surface geoscience.