High-resolution reservoir characterization by 2-D model-driven seismic Bayesian inversion: An example from a Tertiary deltaic clinoform system in the North Sea

In order to retrieve a high-resolution reservoir model from seismic and well data, an approach was developed based on an a priori layered model from well data, specifically the acoustic impedances derived from the sonic and density logs. The procedure consists of using a forward model of the well data as a priori information that is then iteratively matched with the seismic data using a Bayesian inversion process. The inversion is then extended to 2D, whereby the extrapolation is guided by a simple geometric envelope described with a small number of parameters. It is tested on a seismic data set containing a deltaic clinoform in the North Sea, whereby the clinoform geometry is parameterized by a sigmoid and used as prior information. In the subsequent optimization the clinoform geometry is further refined with a limited number of local knots to improve the match with the seismic data. This low-parameterization inversion approach thus uses geological shapes and well constraints to obtain a subsurface model than can have a substantially higher resolution than the seismic wavelength.