Comprehensive framework for gradient-based optimization in closed-loop reservoir management

An efficient, robust, and flexible adjoint-based computational framework for performing closed-loop reservoir management is developed and applied. The methodology includes gradient-based production optimization and data assimilation (history matching). Flexibility is achieved through the use of automatic differentiation (AD) within the reservoir simulation, production optimization, and history matching modules. The use of AD will also facilitate the application of closed-loop reservoir management to physical models of higher complexity. A fast sequential convex programming (SCP) solver based on the method of moving asymptotes (MMA) is applied for the production optimization component of the closed-loop. This technique is shown to outperform the sequential quadratic programming (SQP) method, which is commonly used for production optimization computations. The history matching component of the workflow integrates both production data and proxy seismic measurements into a unified adjoint-based data assimilation framework. The effect of noisy data, and data of different types, on the accuracy of the history matching component is assessed. The overall closed-loop reservoir management methodology is tested using the well-documented Brugge model. Results demonstrate the efficient performance of the individual closed-loop components and the improvement in net present value that is achieved using these procedures.