The ensemble Kalman filter (EnKF) has been used for his- tory matching a simulation model of a North Sea reservoir. Parameters such as initial fluid contacts, vertical transmis- sivity multipliers and fault transmissivity multipliers have been estimated as well as 3D fields of porosity and perme- ability. It is shown that for several of the parameters a large initial uncertainty is reduced to an acceptable level by the assimilation of well-log measurements and production rates of oil, gas and water. The result is an ensemble of history matched realizations which can be used to predict the un- certainty in future production. It is also shown that the formulation used in the EnKF reduces a nonlinear minimization problem in a huge pa- rameter space, involving the minimization of an objective function with multiple local minima, to a statistical mini- mization problem in the ensemble space. Thus, by search- ing for the mean rather than the mode of the posterior pdf, the method avoids getting trapped in local minima and is thus promising for history matching reservoir simulation models. Furthermore, the EnKF provides an ideal setting for operational reservoir monitoring and prediction, including proper representation and prediction of uncertainty.