DE0403-3D : Petroleum Geostatistics - Integrating Data for Reservoir Modeling and Simulation

This practical and highly-interactive course includes various practical sessions and exercises. Theory learnt will be applied using our state-of-the-art simulators.
This course is designed to provide participants with a detailed and up-to-date overview of Petroleum Geostatistics - Integrating Data for Reservoir Modeling and Simulation. It covers the scope of geostatistics in petroleum engineering; the importance of geostatistics for reservoir modeling, applications in oil and gas exploration and development and integration with other disciplines; the fundamentals of reservoir data types covering geological data, geophysical data, reservoir production data and uncertainty and quality of data; and the concepts of heterogeneity and anisotropy, variogram theory, types of variograms and applications of variograms in reservoir modeling.
Further, the course will also discuss the data analysis techniques, histograms, cumulative distributions and cross-plots, normalization; the transformation of reservoir data and detecting outliers ad data inconsistencies; the geostatistical modeling tools and variogram analysis and modeling; the geostatistical data integration and kriging techniques for reservoir estimation; the methods of spatial interpolation in reservoirs; and the advantages and disadvantages of each method.
During this interactive course, participants will learn the sequential gaussian simulation (SGS) and its role in uncertainty quantification; the facies modeling, sequential indicator simulation (SIS) and lithology prediction from well logs; the seismic-to-facies transformations, stochastic and deterministic methods and incorporating seismic attributes into facies models; the fundamentals of multi-point geostatistics (MPS) for complex geological patterns; the petrophysical property modeling, conditional simulations and data conditioning techniques; quantifying and visualizing uncertainty in reservoir modes and probabilistic modeling approaches; the advanced geostatistical techniques, full-field reservoir modeling workflow, history matching and production data integration; validating reservoir models, the sensitivity analysis for model parameters and calibration using production and pressure data; the reservoir simulation workflows, linking static models to dynamic simulators and simulation outputs and interpretation; using geostatistical models for field development planning; the high-uncertainty zones for further exploration; and the risk assessment and mitigation strategies.