A Review of Three Geostatistical Techniques for Realistic Geological Reservoir Modeling Integrating Multi-scale Data

Abstract

Abstract This paper discusses new methodologies and workflows developed to generate geological models (1) that look more realistic geologically speaking and (2) that respect the well and seismic data characterizing the studied area. Accounting simultaneously for these two constraints is challenging as they behave the opposite way. The more realistic the geological model, the more difficult the integration of data. A first powerful approach is based upon the non-stationary plurigaussian simulation method. In this case, the available geological and seismic data make it possible to compute the 3D probability distributions of facies proportions, which are then used to truncate the Gaussian functions. A second method is rooted in the Bayesian sequential simulation. Recent developments have been proposed to extend this method to media including distinct facies. We suggest an improved variant to better account for the resolution differences between sonic logs and seismic data. This yields a more robust framework to integrate seismic data. A third innovative approach reconciles geostatistical multipoint simulation with texture synthesis principles. Geostatistical multipoint methods provide models, which better reproduce complex geological features. However, they still call for significant computation times. On the other hand, texture synthesis has been developed for computer graphics: it can help reduce computation time, but it does not account for data. We then envision a hybrid multi-scale algorithm with improved computation performances and yet able to respect data