Geostatistical Modeling of Reservoir Properties in an Iranian Oil Field from Upper Bangestan Group, Southwest of Iran

Integration of various data types available on different scales by using appropriate statistical tools is necessary for building an appropriate static reservoir model. It is very important prior to the reservoir simulation to estimate reservoir performance uncertainty and to aid reservoir management decisions. The oil field of this study is from upper Bangestan group in the SW of Iran which consists of several reservoirs, including Ilam and Sarvak. The information from 5 wells along with the acoustic impedance in time domain in the field was used to generate stochastic images of porosity and permeability with their inferred associated probability. Data from various scales including logs, cores and AI were available. We used AI to improve the reservoir characterization by providing information on the spatial variation of the reservoir porosity away from the existing well. The velocity model for depth conversion was developed by using first check shots in the wells and second Collocated Co-Kriging method with stacking velocity in the field and the check shots. Variogram contour maps of AI were generated to evaluate the underling anisotropy. These are then used in the framework of the various sequential simulation techniques to produce realizations of 3D porosity and permeability models.