History Matching of Reservoir Models by Ensemble Kalman Filtering: The State of the Art and a Sensitivity Study

History matching is to integrate dynamic data in the reservoir model–building process. These data, acquired during the production life of a reservoir, can be production data, such as well pressures, oil production rates or water production rates, or four-dimensional seismic–related data. The ensemble Kalman filter (EnKF) is a sequential history-matching method that integrates the production data to the reservoir model as soon as they are acquired. Its ease of implementation and efficiency has resulted in various applications, such as history matching of production and seismic data. We focus on the use of the EnKF for history match of a synthetic reservoir model. First, the method of ensemble Kalman filtering is reviewed. Then the geologic and reservoir characteristics of a case study are described. Several experiments are performed to investigate the benefits and limitations of the EnKF approach in building reservoir models that reproduce the production data. Last, special attention is paid to the sensitivity of the method to a set of parameters, including ensemble size, assimilation time interval, data uncertainty, and choice of initial ensemble.