A reliable reservoir geological model serves as the basis for reservoir simulation and production prediction. The accuracy of the reservoir geological model can be significantly improved by incorporating all available data, including the static data and the dynamic data. Traditionally, incorporation of the static data is achieved by using the conditional geostatistical technique, while the dynamic data are honored through history matching. Recently, the Ensemble Kalman Filter (EnKF) technique has been found to be an efficient method for real-time updating the reservoir model. By using the EnKF technique, both the static and dynamic parameters of the reservoir model can be continuously updated by assimilating the measured production data. However, the updated static parameters of the entire reservoir are often found to be inconsistent with the geostatistical simulated results at each time step mainly due to the limitation of the EnKF technique, though the updated static parameters honor the measured data at well locations. In this paper, a novel technique, which integrates the EnKF and the conditional geostatistical technique, is developed and successfully used to dynamically update the reservoir geological model. More specifically, the updated reservoir geostatistical model is constrained to the dynamic data and approaches geologically realistic at each time step by using the EnKF technique. This new technique has been successfully applied in a heterogeneous reservoir. Also, it has been found that the newly developed technique can be used to provide more geologically realistic reservoir models compared with the ones obtained from the EnKF method only.
In the past several decades, reservoir simulation has played an important role in modern reservoir management. The accuracy of the reservoir simulation mainly relies on the accuracy of the reservoir geological model, which is a numerical analog of the real reservoir system. Furthermore, in order to analyze the uncertainty of a given reservoir development scenario, multiple geological models should be provided. Traditionally, the conditional geostatistical technique1 is utilized to incorporate static data and generate a number of equiprobable realizations of a reservoir geological model; however, dynamic production data are difficult to be honored through the geostatistical simulation.
To incorporate the dynamic data into a reservoir geological model, history matching method is utilized to tune the model to match the past performance of reservoir history. Traditional history matching is a time-consuming and skill-demanding manual process. In addition, such a manual history matching process for multiple geological realizations is impracticable. As a result, assisted history matching technique has been proposed to accelerate and improve the matching process. In particular, the Ensemble Kalman Filter (EnKF) technique2, 3 has been found to be an efficient assisted history matching method.
In this paper, a novel technique, which integrates the EnKF and the conditional geostatistical simulation technique, is developed to dynamically update the reservoir geological model. More specifically, the updated reservoir geostatistical model is constrained to the dynamic data, such as reservoir pressure and fluid saturations, and approaches geologically realistic at each time step by using the EnKF technique. This new technique has been successfully applied in a heterogeneous synthetic reservoir.
