Agradecimentos: The authors thank the following entities for supporting this research: PETROBRAS; the Research Network SIGER; the National Agency of Petroleum, Natural Gas and Biofuels (ANP); the Center for Petroleum Studies (CEPETRO), and the UNISIM Research Group; the Department of Energy of the...

Agradecimentos: The authors thank the following entities for supporting this research: PETROBRAS; the Research Network SIGER; the National Agency of Petroleum, Natural Gas and Biofuels (ANP); the Center for Petroleum Studies (CEPETRO), and the UNISIM Research Group; the Department of Energy of the School of Mechanical Engineering of the University of Campinas (DE-FEM-UNICAMP); and the Coordination for the Improvement of Higher Education Personnel (CAPES). The authors also thank the Computer Modelling Group Ltd. (CMG), Beicip-Franlab and Mathworks for software licenses and technical support

During the development of petroleum fields, uncertainty quantification is essential to base decisions. Several methods are presented in the literature, but its choice must agree with the complexity of the case study to ensure reliable results at minimum computational costs. In this study, we...

During the development of petroleum fields, uncertainty quantification is essential to base decisions. Several methods are presented in the literature, but its choice must agree with the complexity of the case study to ensure reliable results at minimum computational costs. In this study, we compared two risk analysis methodologies applied to a complex reservoir model comprising a large set of geostatistical realizations: (1) a generation of scenarios using the discretized Latin hypercube sampling technique combined with geostatistical realizations (DLHG) and (2) a generation of scenarios using the Monte Carlo sampling technique combined with joint proxy models, entitled the joint modeling method (JMM). For a reference response, we assessed risk using the pure Monte Carlo sampling combined with flow simulation using a very high sampling number. We compared the methodologies, looking at the (1) accuracy of the results, (2) computational cost, (3) difficulty in the application, and (4) limitations of the methods. Our results showed that both methods are reliable but revealed limitations in the JMM. Due to the way the JMM captures the effect of a geostatistical uncertainty, the number of required flow simulation runs increased exponentially and became unfeasible to consider more than 10 realizations. The DLHG method showed advantages in such a context, namely, because it generated precise results from less than half of the flow simulation runs, the risk curves were computed directly from the flow simulation results (i.e., a proxy model was not needed), and incorporated hundreds of geostatistical realizations. In addition, this method is fast, straightforward, and easy to implement

COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES

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