Ranking of Stochastic Seismic Inversion Realizations Using Streamline Simulations
Authors: Ali Virk Muhammad Muneeb 
Published: November 2011

Abstract
Because stochastic seismic inversion generates a large number of elastic impedance realizations, only a few of these realizations must be selected for more rigorous geological interpretation and reservoir modelling. Static sand body volume (SBV) and SBV geometrically connected to the wells are generally used as the ranking measures. However, this approach does not take into account the dynamic flow field and the impact of reservoir heterogeneity and petrophysical properties on the fluid flow. Hence, this approach under or overestimates the P10, P50 & P90 ranking most of the time and invalidates the field development studies in turn.
The main goal of this study is to prove the concept of using dynamic measures to rank the seismic inversion realizations and develop a ranking work flow. Streamline simulations, because of its generic option of reduced flow physics provides a faster way to run hundred of millions cell realizations, enabling a quick ranking based on dynamic measures.
Typical seismic inversion models consists of several million cells (6 million in this case), and flow simulation over models of this size is computationally extensive. To reach reasonable runtimes, the size of the model is reduced; either by extracting a sector model (2 million cells sector in this case), or by decimating/up-scaling the full field model (1.7 million cells in this case). Tracer simulations over both of the reduced size models are compared with the full field tracer simulation model. The upscaled model is shown to be a closer approximation to the full field model. Finally, the tracer upscaled model is compared with a physically more realistic waterflood upscaled simulation model and is shown as a valid approximation.
The sensitivity of various dynamic parameters to connectivity variations is analyzed to determine which parameter is the best suited ranking measure. Due to significant variability in spatial sand bodies distribution and connectivity between the sand bodies across the realizations, cumulative oil recovery (COR) has proved to be a much better ranking measure in comparison to the recovery factor (RF). The static and dynamic ranking measures are compared. The difference between SBV or connected sand body volume (CSBV) and COR rankings highlights the importance of ranking based on dynamic measures. Additionally, the factors which can generally influence the ranking based on dynamic measure (COR) are determined and it is concluded that the changes in ranking will be dependent on the CSBV and petrophysical properties of the connected sand bodies across the realizations.
Finally, ranking based on the best ranking measure as well on a combination of COR, STOIIP and RF is done and individual realizations (P10, P50 and P90) are selected in order to cover the range of uncertainty in spatial distribution and volumes of sand bodies. This uncertainty range can be translated into economic uncertainty for the determined optimum number of wells and distances.
The approach defined in this paper to rank and select the P10, P50 & P90 realizations based on dynamic measures is generic and can be applied to any form of geo-statistical realizations. The streamline simulations proved to be an effective screening tool.