Subsurface models of lithology are often poorly constrained due to the lack of dense well control. Although limited in vertical resolution, high-quality 3-D seismic data usually provide valuable information regarding the lateral variations of lithology. In this paper, we will show how the Bayesian approach can be used to generate seismically constrained models of lithology. Unlike cokriging-based simulation methods, this method does not rely on a generalized linear regression model, which is inadequate when combining discrete variables, such as lithology indicator, and continuous variable, such as seismic attributes. This method uses a Bayesian updating rule to construct a posterior probability distribution function (pdf) of lithoclasses by using a priori information from well data and the seismic likelihood to constrain the 3-D geological scenarios produced by geostatistical technique, which is then sampled sequentially at all points in space to generate a set of realizations. The realizations define alternative, equiprobable lithologic models. The methodology was applied to delineate productive reservoir zone in Boonsville Field, Texas. To achieve better result in the Bayesian sequential indicator simulation (BSIS), we use acoustic impedance obtained from a seismic inversion process as the attribute to constrain the simulations. It is expected that by using this attribute, the separation of the litho class-conditional distribution will be better defined compared to using amplitude map, and at the same time minimizing the overlaps between the two distributions. The lithology classification obtained from BSIS is then integrated with the result of the seismic inversion to clearly delineate the productive reservoir zone in the field.
Keywords: Bayesian sequential indicator simulation, seismic inversion, Boonsville Field.
Author : Befriko Murdianto, Leonard Lisapaly, Abdul Haris