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 and core measurements into reservoir models. However, approaches to integrate dynamic/production data efficiently into these models are largely tacking. 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 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 2D and 3D reservoir.