Iterative updating of reservoir models constrained to dynamic data

Geostatistical algorithms are being widely used to integrate different data such as seismic amplitude, well logs & core measurements into reservoir models. However approaches to integrate dynamic/production data efficiently into these models are largely lacking. Production data differs from other types of static data (such as porosity, permeability, amplitude, etc.) primarily because they are non-linearly related to the connectivity characteristics of the reservoir. In this paper we develop a gradual deformation methodology to integrate two-phase production data in order to give rise to a suite of reservoir models that are conditioned to both the static data as well as dynamic data. We utilize the Sequential Indicator Simulation algorithm within a non-stationary Markov Chain to iteratively update the realizations till a history match is obtained. The methodology is tested on a synthetic 2-D and 3-D reservoir.