Optimizing objectives of long-term reservoir management typically requires a high number of reservoir simulations. Two important remedies to reduce the runtime (and make the optimization problem manageable) are model reduction/upscaling and efficient computation of gradients. Adjoint methods are generally considered to be the most efficient means for obtaining gradients. In this work, we employ a global approach to compute upscaled transmissibilities for a coarse model. The proposed coarsening strategy takes as input any number of fine-scale states (pressure fields from, e.g., a previous simulation), and produces a coarse-scale model calibrated to the specific flow scenario(s) dictated by the input. We argue that compared to traditional general-purpose upscaling approaches, more aggressive coarsening can be applied in this type of scenario-specific upscaling. Utilizing a fully-implicit, three-phase, black-oil simulator with adjoint capabilities, we investigate the performance of our methodology by optimizing the net-present-value (NPV) for a real-field model. For the optimization approach, we develop a control switching approach for efficient handling of output constraints. We consider moderately upscaled models, and conclude that for the case we consider, at least two orders of magnitude speed-up can be achieved whilst retaining sufficient accuracy.