Using an objective function to guide the parameterization of a stratigraphic forward model

Abstract

Building representative geological models of reservoirs is a complex task, especially while using traditional geostatistical modeling methods due to data limitations. Stratigraphic Forward Modeling (SFM) enhances the accuracy of models by incorporating geologic and depositional concepts, resulting in greater applicability. However, the method struggles with well data integration and definition of simulation input parameters which are not easily drawn from usual available data or conceptual modeling. Hence, there are uncertainties related to SFM input parameters and the reliability of results. In this work, SFM multi-realizations performed by DionisosFlow™ were analyzed through an objective function that measures similarity between facies successions (stratigraphic correlation objective function – SCOOF) to compose an empirical methodology that performs the adjustment of SFM models to well data. A set of scenarios was assembled by varying a group of selected uncertain parameters. These scenarios were submitted to SCOOF calculation and parameter values were taken from those that gave lower SCOOF values. By re-parameterizing the initial model with chosen values, thickness and lithology deposition improvements in wells were obtained and validated by the decline of objective function values from the initial to the final model.