An Application of Geostatistics in Subsurface Characterization using Hard Data and Soft Information

Abstract

The reliability of flow and transport models depends on the understanding of the characteristics and impacts of spatial structures of subsurface formations. Preferential pathways are controlled by the geometries of these units. In subsurface characterization, it is the current practice to use both interpolation and extrapolation using a series of observations from well logs. Classical techniques, such as weighted interpolation and least-squares polynomial, are not recommended because they assume independence of sample data. Geostatistics methods bring a new horizon for reservoir engineering studies. Spatial dependence or autocorrelation of data exist, given the fact that neighbouring points tend to present similarities. In addition to this fact, reservoir heterogeneities are caused by usually known geologic processes such as deposition, sediment diagenesis and fracturing. Thus geologic data and models should also be helpful in characterizing spatial variability of flow properties. This work applies a Bayesian Kriging technique which includes an expertise guess with a given uncertainty in the estimation procedure. This technique assures that observation data (hard data) prevail in areas close to field measurements, whereas in areas without observations the expert’s experience (soft data) have greater influence. Maps of the estimates and the associated uncertainties are shown to be key tools in reflecting the quantity and quality of the available. Simple Kriging and Universal Kriging become subsets of this procedure.