SPE 112873 Efficient Ensemble-Based Closed-Loop Production Optimization

With the advances in smart well technology, substantially higher oil recovery can be achieved by intelligently managing the operations in a closed-loop optimization framework. The closed-loop optimization consists of two parts: geological model updating and production optimization. Both of these parts require gradient information to minimize or maximize an objective function: squared data mismatch or the net present value (or other quantities depending on financial goals), respectively. Alternatively, an ensemble-based method can acquire the gradient information through the correlations provided by the ensemble. Computation of the optimal controls in this way is nearly independent of the number of control variables, reservoir simulator and simulation solver. In this paper, we propose an ensemble-based closed-loop optimization method that combines a novel ensemble-based optimization scheme (EnOpt) with the ensemble Kalman filter (EnKF). The EnKF has recently been found suitable for sequential data assimilation in large-scale nonlinear dynamics. It adjusts reservoir model variables to honor observations and propagates uncertainty in time. The EnOpt optimizes the expectation of the net present value based on the updated reservoir models. The proposed method is fairly robust, completely adjoint-free and can be readily used with any reservoir simulator. The ensemble-based closed-loop optimization method is illustrated with a waterflood example subject to uncertain reservoir description. Results are compared with other possible reservoir operation scenarios, such as, wells with no controls, reactive control, and optimization with known geology. The comparison shows that the ensemble-based closed-loop optimization is able to history match the main geological features and increase the net present value to a level comparable with the hypothetical case of optimizing based on known geology. Introduction Production optimization offers the potential to substantially increase ultimate oil recovery by developing an improved operating plan for a particular reservoir of interest. Since the economic objective involves evaluations of future achievements, it requires a reservoir simulation model for prediction and is usually referred to as a modelbased optimization technique. Due to limited access to the reservoir, the reservoir geological model is subject to high uncertainty. In order to obtain a suitable production strategy for the reservoir of interest, production optimization needs to be combined with a parameter estimation method that reduces the uncertainty of the estimate of the reservoir geological properties. Closed-loop optimization (Brouwer et al., 2004; Sarma et al., 2005b; Wang et al., 2007) combines production optimization with data assimilation to form a real time reservoir management. Data assimilation is a sequential model updating method, where the estimate of the uncertain parameters is updated continuously to be consistent with the production data available in time. The workflow of the closed-loop optimization technique is typically as follows (see Fig. 1). An initial geological model is built using available prior knowledge of the reservoir and the initial production strategy is chosen based on this prior knowledge. At times when production data are available, the reservoir geological model is updated, and the updated geological model provides the basis for a better estimate of the true reservoir behavior. The production strategy is then optimized based on the newly updated reservoir model. This process can be carried out in real time, and the management of the reservoir is kept up-to-date. Both model updating and production optimization are optimization problems. Model updating aims at minimizing the mismatch between the model predictions and the historical production data. It is commonly known as