An Automatic History Matching Module with Distributed and Parallel Computing

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 a few decades. However, limited success has been achieved due to the high complexity of the problem and the large computational effort required in the real fields. Successful applications of the ensemble Kalman filter (EnKF) to reservoir history matching have been reported in various publications. The EnKF is a sequential method: once new data are available, only these data are used to update all the unknown reservoir properties while previous geological information is unused directly. In this method, multiple reservoir models rather than one single model are implemented, and each model is called a member. Conventionally, the impact of each member on the updating is equally treated. Another approach is the weighted EnKF. During the updating, the method weighs the contribution of each member through the comparison between the simulation response and the measurements. Better matching performance has been found in the weighted EnKF than in the conventional EnKF. To improve computational efficiency, two-level high-performance computing for reservoir history matching process is implemented in this research, distributing ensemble members simultaneously while simulating each member in a parallel style. An automatic history-matching module based on the weighted EnKF and high-performance computing is developed and validated through a synthetic case operating from primary, waterflooding to flooding of water alternating with gas. The study shows that the weighted EnKF improves the matching results, and the high-performance computing process significantly reduces the history matching execution time.