1 9th European Conference on the Mathematics of Oil Recovery — Cannes, France, 30 August - 2 September 2004 Abstract Closed-loop waterflooding involves the combined use of model-based optimization techniques and data assimilating techniques, which are used to maximize ultimate recovery or net-present value (NPV) and to update the reservoir model during the producing life of the reservoir. In particular we used Optimal Control Theory (OCT) in combination with an Ensemble Kalman Filter (EnKF) to perform closed-loop water flooding. Testing the effectiveness of the closed-loop approach was done on a synthetic reservoir model (SRM), mainly because of repeatability and time aspects. We used the SRM to generate synthetic data such as production measurements and time-lapse seismics. As a first step we used simple, two-dimensional models to demonstrate the feasibility of this workflow, i.e. the use of closed-loop reservoir management techniques combined with a SRM. We used a commercial reservoir simulator as the SRM, and an in-house Matlab reservoir model for the OCT and EnKF. The EnKF uses an ensemble of model representations to update the permeability field and the states (pressures and saturations) of the reservoir from ‘measured’ pressures from the SRM. OCT is then applied, using the most recently estimated permeability field, to optimize injection and production rates in a fixed configuration of wells. We obtained an increase in NPV of 29% after injecting one pore volume, as a result of a higher oil production (increase of 20%) and a lower water production (decrease of 34%). The estimated permeability field revealed the main heterogeneities of the SRM. The use of the SRM allowed for rapid testing of different varieties of OCT and EnKF techniques. 1 Introduction In order to establish the most favorable development and management of a hydrocarbon reservoir, we usually use a numerical reservoir model. These models are a crude approximation of reality and frequently contain a large amount of uncertainties. History matching is a way to adapt the parameters of the model, but it is usually done on a campaign basis, for example, once every five years. With data assimilation techniques we can shift from a campaign-based ad-hoc history matching procedure to a near-continuous systematic updating of the reservoir parameters based on data from different sources during the producing life of the reservoir. In our study we have only used pressure and flowrate measurements as data, but the scope for expansion in data sources is wide, e.g. the use of time-lapse seismics or passive seismics. In a numerical model for water flooding we can systematically optimize reservoir production strategies by using smart field technology, in particular when we use model- based optimization techniques to steer the waterfront in the reservoir and in this way optimize the ultimate recovery or net-present value (NPV). In this study we will combine model-based optimization techniques and data assimilating techniques. In particular we will use Optimal Control Theory (OCT) as described by Brouwer et al. [1], in combination with an Ensemble Kalman Filter (EnKF) to perform closed-loop water flooding. The EnKF uses an ensemble of model representations to update the reservoir states (pressures and saturations) and parameters (permeabilities), and the associated model uncertainty. With the most recent updated states and parameters, we will then apply OCT to optimize injection and production controls in a fixed configuration of wells. The pressure measurements from downhole B033 CLOSED-LOOP WATERFLOODING K.M. OVERBEEK*, D.R. BROUWER* , G. NAEVDAL ‡ , C.P.J.W. VAN KRUIJSDIJK* , J.D. JANSEN* † *Delft University of Technology, Dept of Geotechnology, PO box 5028, 2600 GA Delft, The Netherlands † Shell International Exploration and Production, Exploratory Research, PO box 60, 2280 AB Rijswijk ‡ RF – Rogaland Research, Thormøhlensgate 55, N-5008 Bergen, Norway