Joint History Matching of Production and Tracer Data Through an Iterative Ensemble Smoother: A 3D Field-Scale Case Study

Abstract

Abstract Reservoir models are often subject to uncertainties, which, if not properly taken into account, may introduce biases to the subsequent reservoir management process. To improve reliability and reduce uncertainties, it is crucial to condition reservoir models on available field datasets through history matching. There are different types of field data. Among others, production data are the most common choice, but they are subject to a major limitation of carrying relatively low value of information. On the other hand, inter-well tracer data have been shown to provide additional information about well-to-well connectivity and reservoir dynamics. However, jointly history matching production and inter-well tracer data still remains challenging due to the lack of a coherent quantitative workflow to fully integrate them. This work can be considered a step towards tackling this noticed problem. To this end, we propose a non-intrusive and derivative-free ensemble history matching workflow, in which reservoir models are more coherently conditioned on both production and inter-well tracer data with the help of a recently developed technique (adaptive localization). The workflow is successfully implemented in the Brugge benchmark case. Our study indicates that the history matching algorithm matches the production data well, regardless of the presence or absence of the tracer data. Nevertheless, by including tracer data as an additional source of information, we are able to improve the quality of the estimated reservoir models, in terms of both improved data match and reduced model discrepancies. Furthermore, we show that the proposed workflow is robust and provides a reasonably good way of uncertainty quantification. In summary, with the help of the adaptive localization scheme, we are able to address the issues of proper uncertainty quantification, and more coherent utilization of different types of field datasets.