In the modern oil industry, geostatistical property models are built for different purposes such as resource estimation and flow simulation. Processing of multiple realizations, obtained from geostatistical simulation techniques, helps assess uncertainty analysis which is important for development planning and decision-making processes.

Each geological model is a combination of structural, facies, and attributes models. In the case of conventional flow simulation (i.e. without considering geomechanical simulation), the petrophysical properties porosity, permeability and saturation, are the only attributes necessary to model. These parameters are included in the fluid flow governing equations. But in the case of dealing with coupled geomechanical-flow simulation, rock mechanical properties are also required.

In the case of conventional simulation process, geostatistical property models have been used widely, but in the case of coupled geomechanical-flow simulation processes, geostatistical modeling for geomechanical attributes has yet to be incorporated. Therefore, uncertainty assessment could be underestimated according to the spatial distribution of these parameters.

In this work, the effect of heterogeneous geomechanical properties on coupled geomechanical-flow simulation process was investigated for a steam assisted gravity drainage (SAGD) process for a heavy oil reservoir in Alberta-Canada.

Cumulative oil Production (COP), Steam Oil Ratio (SOR) and Vertical Deformation Profile (VDP) of the top of reservoir is considered as three simulation output variables. Consideration of heterogeneous models for both flow and geomechanical properties in coupled geomechanical flow simulation of the SAGD process resulted in a range of uncertainties for these three variables. The importance of considering geomechanical properties as heterogeneous models is illustrated by comparing these ranges with the ranges obtained from coupled simulations in which geomechanical properties are considered as homogeneous models. Representative synthetic data of a sand/shale spatial distribution of McMurray formation in Alberta-Canada is considered for the case study.

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