Agradecimentos: The authors are grateful to Petrobras (Grant Agreement No. 0050.0100204.16.9), Energi Simulation, the National Agency of Petroleum, Natural Gas and Biofuels (ANP), the Center of Petroleum Studies (CEPETRO-UNICAMP), the UNISIM research group, the Department of Energy of the School of...

Agradecimentos: The authors are grateful to Petrobras (Grant Agreement No. 0050.0100204.16.9), Energi Simulation, the National Agency of Petroleum, Natural Gas and Biofuels (ANP), the Center of Petroleum Studies (CEPETRO-UNICAMP), the UNISIM research group, the Department of Energy of the School of Mechanical Engineering (UNICAMP) for supporting this research, and also, to the Computer Modelling Group (CMG) for software licenses

This paper introduces a new method for history matching, combining the Iterative Discrete Latin Hypercube (IDLHC) with multi-start Simulated Annealing methods. The proposed method, named IDLHCSA, combines the potential of the IDLHC in finding good matched models while preserving the diversity of...

This paper introduces a new method for history matching, combining the Iterative Discrete Latin Hypercube (IDLHC) with multi-start Simulated Annealing methods. The proposed method, named IDLHCSA, combines the potential of the IDLHC in finding good matched models while preserving the diversity of solutions and the potential of the SA with local control in finding local (refined) solutions. The IDLHCSA was applied in two cases. The first is a simple reservoir model used as proof of concept. The second is a complex benchmark case (UNISIM-I-H) based on the Namorado field, located at the Campos Basin, Brazil. The robustness and effectiveness of the proposed method are demonstrated by comparison with other consolidated methods. It is demonstrated here that, when compared to other methodologies, the proposed method is more effective in finding multiple solutions for the history matching problem while maintaining solution diversity. The production forecast is analyzed and the predictive capacity of the matched models is assessed. The paper reveals that obtaining good matched models does not ensure reliable forecasts. The proposed method was able to find matched models which provided more reliable forecasts when compared to other methods

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