Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems

Focusing on the application of machine and deep learning to subsurface geological challenges, this book delves into prediction models using well-log data for resource evaluation and reservoir characterization. It details methods to enhance model performance and optimize sparse datasets. Key topics include estimating total organic carbon, predicting brittleness indexes, and assessing carbon storage reservoirs, each supported by case studies. The chapters integrate geological context with a comprehensive literature review, making it a valuable resource for professionals in subsurface geoscience.