Agradecimentos: The authors would like to thank the following entities for supporting this research: Petrobras S/A, and the Research Network SIGER; the National Agency of Petroleum, Natural Gas and Biofuels (ANP); the Center for Petroleum Studies (CEPETRO), and UNISIM Research Group; the Department...

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

In this study, we consider information acquisition to manage uncertainty in reservoir development projects. Expected Value of Information (EVoI), a long-standing and widely applied technique, is traditionally used to base the decision to acquire additional data. In the field development phase, this...

In this study, we consider information acquisition to manage uncertainty in reservoir development projects. Expected Value of Information (EVoI), a long-standing and widely applied technique, is traditionally used to base the decision to acquire additional data. In the field development phase, this calculation can be very complex, a consequence of high uncertainty and multiple decision variables. We propose a methodology to facilitate and reduce the subjectivity of the EVoI analysis, eliminating misconceptions and bias towards particular uncertainties and information sources. We provide indicators to identify, a priori, the uncertainties that can be mitigated with information, on which the EVoI calculation is based. Our workflow considers all available information sources, and integrates the concepts of individual, simultaneous and sequential acquisition of information, and estimates EVoI for perfect and imperfect information. We use a predefined set of candidate production strategies, previously optimized for representative models, which reflect system inputs and outputs. These combined approaches enable automating the EVoI calculation. We applied the methodology to the synthetic reservoir model UNISIM-I-D, a complex case in the development phase. Although there seemed to be great gains with an additional appraisal well, the economic gains from the improved decision did not compensate for delayed production. This supports the importance of performing the EVoI calculation, and not relying on the assumption that more information is always preferable

COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES

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