The process of generating reservoir models that consistently honors static and dynamic data is currently a time consuming process that can take several years to complete. This follows since the modelling and data-conditioning work is frequently done in a stepwise manner, with a high degree of customization in the different parts of the process. As a result, these models are difficult to update when new data arrives, which in practice means that business critical decisions have to be made based on outdated models. In this paper, we discuss the flaws of the current established best practices in reservoir modelling and data conditioning, and discuss how to solve these challenges in an efficient manner.


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