Ensemble-based methods generate interesting results regarding history-matching processes. However, there are several parameters that can have a large impact on the final response of the process. Therefore, this work presents a comprehensive study of the following parameters of an ensemble-based...

Ensemble-based methods generate interesting results regarding history-matching processes. However, there are several parameters that can have a large impact on the final response of the process. Therefore, this work presents a comprehensive study of the following parameters of an ensemble-based method, the ES-MDA: different petrophysical images in the initial ensemble, inflation factor (α), measurement error (CD) and truncated singular value during the matrix inversion process (TSV); aiming to understand how each parameter affects data match, uncertainty reduction, and production forecast. Results showed that different initial ensembles have a major impact when using a small number of models, and minor effects for a large number of models. Inflation factors and CD have a high impact on the whole process (data match, uncertainty reduction, and production forecast), and TSV has a minor impact on the process. Finally, careful analysis of these parameters and further research is required to obtain better results

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