Author: Baosheng Liang

Publisher:

Published: 2007

Total Pages: 0

ISBN-13:

The data assimilation process of adjusting variables in a reservoir simulation model to honor observations of field data is known as history matching and has been extensively studied for few decades. However, limited success has been achieved due to the high complexity of the problem and the large computational effort required by the practical applications. An automatic history matching module based on the ensemble Kalman filter is developed and validated in this dissertation. The ensemble Kalman filter has three steps: initial sampling, forecasting through a reservoir simulator, and assimilation. The initial random sampling is improved by the singular value decomposition, which properly selects the ensemble members with less dependence. In this way, the same level of accuracy is achieved through a smaller ensemble size. Four different schemes for the assimilation step are investigated and direct inverse and square root approaches are recommended. A modified ensemble Kalman filter algorithm, which addresses the preference to the ensemble members through a nonequally weighting factor, is proposed. This weighted ensemble Kalman filter generates better production matches and recovery forecasting than those from the conventional ensemble Kalman filter. The proposed method also has faster convergence at the early time period of history matching. Another variant, the singular evolutive interpolated Kalman filter, is also applied. The resampling step in this method appears to improve the filter stability and help the filter to deliver rapid convergence both in model and data domains. This method and the ensemble Kalman filter are effective for history matching and forecasting uncertainty quantification. The independence of the ensemble members during the forecasting step allows the benefit of high-performance computing for the ensemble Kalman filter implementation during automatic history matching. Two-level computation is adopted; distributing ensemble members simultaneously while simulating each member in a parallel style. Such computation yields a significant speedup. The developed module is integrated with reservoir simulators UTCHEM, GEM and ECLIPSE, and has been implemented in the framework Integrated Reservoir Simulation Platform (IRSP). The successful applications to two and three-dimensional cases using blackoil and compositional reservoir cases demonstrate the efficiency of the developed automatic history matching module.