Agradecimentos: The authors are grateful to the Center of Petroleum Studies (CEPETRO-Unicamp/Brazil), PETROBRAS S/A, UNISIM, ANP, Petroleum Engineering Department (DEP-FEM-Unicamp/Brazil) and Energi Simulation for their support of this work. The authors are also grateful to Schlumberger Information...

Agradecimentos: The authors are grateful to the Center of Petroleum Studies (CEPETRO-Unicamp/Brazil), PETROBRAS S/A, UNISIM, ANP, Petroleum Engineering Department (DEP-FEM-Unicamp/Brazil) and Energi Simulation for their support of this work. The authors are also grateful to Schlumberger Information Solution for the use of Petrel® and to Computing Modeling Group for the use of Builder CMG®

Different types of static and dynamic uncertainties can play critical roles to select an efficient and reliable production strategy in the oil fields, yielding a large number of models to analyze. The definition of such a production strategy requires flow simulation for each model. Since the flow...

Different types of static and dynamic uncertainties can play critical roles to select an efficient and reliable production strategy in the oil fields, yielding a large number of models to analyze. The definition of such a production strategy requires flow simulation for each model. Since the flow simulation process for this large model ensemble requires a great CPU effort, selecting a subset of representative models (RMs) is often used for accelerating the analysis. However, providing an applicable and efficient method to identify the RMs that statistically reflect the same dynamic and static properties as the full set of models is still challenging. In this study, we develop a new workflow to select RMs based on the integration of distance based clustering and data assimilation aimed at the field development. The workflow begins with the generation of geostatistical models using Latin hypercube (LH) sampling and then reducing them through distance-based clustering according to their static features. Therefore, there is no need to apply numerical simulation for scenario reduction. Next, discrete Latin hypercube with geostatistical realizations (DLHG) method is performed to combine other types of uncertainty with the reduced geostatistical models and build the simulation models. Eventually, data assimilation (DA) is applied to select the subset of models that match the past reservoir performance and production data. In addition, the uncertainty range are evaluated for the defined well and field objective functions using the cumulative distribution function (CDF) and non-parametric Kolmogorov-Smirnov (K-S) test. The methodology is successfully applied to a synthetic benchmark case named UNISIM-II-D considering the flow unit modeling. The results show that the workflow can effectively and suitably identify the RMs considering different uncertainty types, reservoir heterogeneity and many field and well objective functions. The selected RMs are used for making production forecasts and development planning support

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