Reservoir management typically focuses on maximizing oil and gas recovery from a reservoir based on facts and information while minimizing capital and operating investments. Modern reservoir management uses history-matched simulation model to predict the range of recovery or to provide the economic assessment of different field development strategies. Geological models are becoming increasingly complex and more detailed with several hundred thousand to million cells, which include large sets of subsurface uncertainties. Current issues associated with history matching, therefore, involve extensive computation (flow simulations) time, preserving geologic realism, and non-uniqueness problem. Many of recent rate optimization methods utilize constrained optimization techniques, often making them inaccessible for field reservoir management. Field-scale rate optimization problems involve highly complex reservoir models, production and facilities constraints and a large number of unknowns. We present a hierarchical multiscale calibration approach using global and local updates in coarse and fine grid. We incorporate a multiscale framework into hierarchical updates: global and local updates. In global update we calibrate large-scale parameters to match global field-level energy (pressure), which is followed by local update where we match well-by-well performances by calibration of local cell properties. The inclusion of multiscale calibration, integrating production data in coarse grid and successively finer grids sequentially, is critical for history matching high-resolution geologic models through significant reduction in simulation time. For rate optimization, we develop a hierarchical analytical method using streamline-assisted flood efficiency maps. The proposed approach avoids use of complex optimization tools; rather we emphasize the visual and the intuitive appeal of streamline method and utilize analytic solutions derived from relationship between streamline time of flight and flow rates. The proposed approach is analytic, easy to implement and well-suited for large-scale field applications. Finally, we present a hierarchical Pareto-based approach to history matching under conflicting information. In this work we focus on multiobjective optimization problem, particularly conflicting multiple objectives during history matching of reservoir performances. We incorporate Pareto-based multiobjective evolutionary algorithm and Grid Connectivity-based Transformation (GCT) to account for history matching with conflicting information. The power and effectiveness of our approaches have been demonstrated using both synthetic and real field cases.