Efficient Ensemble-Based Closed-Loop Production Optimization

Mork Family Department of Chemical Engineering and Materials Science Distinguished Lecture SeriesPresentsProfessor Dean OliverMewbourne School of Petroleum and Geological Engineering,
University of Oklahoma, Norman, OK 73019Abstract:
With the advances in smart well technology, substantially higher oil recovery can be achieved by intelligently designing the operation scheme 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 different financial goals), respectively. Alternatively, an ensemble-based method can acquire the gradient information through the correlations provided by the ensemble. Computation in this way is nearly independent of the number of control variables, reservoir simulator and simulation solver. We propose a new method for closed-loop optimization, which combines an ensemble based optimization scheme (EnOpt) with the ensemble Kalman filter (EnKF). The EnKF adjusts the reservoir models to honor observations and propagates the uncertainty in time. EnOpt optimizes the expectation of the net present value based on the updated reservoir models. The combined method is robust, completely adjoint-free and can be readily used with any reservoir simulator. The proposed scheme is illustrated with the Brugge Field test example developed for the SPE-ATW on Closed-Loop Optimization. This test problem has 10 injectors, 20 producers and 3 completion intervals per well that can be controlled independently.

Location: Olin Hall of Engineering (OHE) - 122

Audiences: Everyone Is Invited

Contact: Petra Pearce Sapir