Perpustakaan Digital ITB

A history matching is a process to calibrate the model with the observed data in order to adjust a model of reservoir until it can reproduce as closely as possible the behavior of the reservoir based on history or production data. The common history matching in the reservoir simulation, as so-called a conventional history matching, processes the calibration manually. The matching process is adjusted with trial and error by changing the permeability of a model until the model responds similar to the behavior of the real reservoir. However, this matching process is often timeconsuming. In the other hand, the inverse modeling method offers an alternate way of constructing the permeability distribution in a reservoir model by using production history data as input in the calibration process. This matching process is then considered to be less-consuming and real time. Ensemble Kalman Filter (EnKF) is one of the inverse modeling method that can be proposed for an alternate way of doing simulation. The research is accomplished to evaluate the application of EnKF method in the reservoir geothermal simulation based on production history data. Two experimental models are constructed and simulated using TOUGH2 reservoir simulation. One of the experimental models is assigned with single permeability in all grids, while the other one is assigned with various permeability value distributed in all grids of model. It is also performed several data assimilation scenarios. It indicates that EnKF method can be applied in history matching process to inverse model permeability from geothermal reservoir. It also shows that pressure data assimilation gives a better matching than temperature data assimilation. However, EnKF method may give non-unique outcomes or solutions for the very great range of permeability in a model. The addition of extra information (a priori information) to the inversion process or data assimilation is proven to diminish the effect of nonuniqueness.