Analysis of the performance of ensemble-based assimilation of production and seismic data

Ensemble-based methods have gained popularity as reservoir history-matching techniques. The advantages typically attributed to these methods include the possibility of adjusting a large number of model parameters at a reasonable computational cost, the generation of several alternative models conditioned to data and the ease of implementation. In fact, it is straightforward to adapt these methods to handle different types of data and model variables. Moreover, they are easily coupled with commercial reservoir simulators. Among these methods, the ensemble Kalman filter (EnKF) is by far the most investigated. Iterative forms of the ensemble smoother (ES), on the other hand, are less widespread in the literature. However, ensemble smoothers are much better suited to practical history-matching applications, because they do not require updating dynamical (state) variables and consequently avoid the frequent simulation restarts required by EnKF. This paper presents the results of an investigation on the performance of a variant of ES, namely, ensemble smoother with multiple data assimilation (ES-MDA), to history match production and seismic data of a real field. The paper discusses the quality of the data matches, the plausibility of the history matched models, the ability of the posterior ensemble to assess the uncertainty in the forecasted water production, the effect of the number of iterations and localization. The paper also includes two appendix sections. The first one presents two alternative implementations of the ES-MDA method. The second appendix presents the matrix operations for an efficient implementation of the analysis.