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.
All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)
- Fuel Technology
- Energy Engineering and Power Technology