Spatial data science and geostatistics play a critical role in the oil and gas industry by providing a quantitative framework for reservoir forecasting and uncertainty quantification. By integrating diverse data sources — from well data to field-scale seismic attributes — these methods support robust reservoir modeling and informed decision-making. Professor Tapan Mukerji offers this five-day course for professionals seeking a deeper understanding of the concepts and applications of geostatistical algorithms in reservoir modeling. The objective of the course is to introduce participants to key contemporary geostatistical techniques used in reservoir characterization and modeling. The underlying mathematical principles will be briefly reviewed to provide intuitive insight, while strong emphasis is placed on conceptual understanding and practical implementation. Participants will learn the capabilities and limitations of different algorithms, enabling more effective application in real projects. Hands-on exercises will be conducted using the open-source SGeMS (Stanford Geostatistical Modeling Software) platform, allowing attendees to gain practical experience with each algorithm and understand the importance and sensitivity of key input parameters.