The coming years there is a need to increase production from petroleum reservoirs, and there is an enormous potential to do so by increasing the recovery factor. This is possible by making better use of recent technological developments, such as horizontal wells, downhole valves and sensors. However, actually making better use of these improved capabilities is difficult because of many open problems in reservoir management and production operations processes. Consequently, there is significant scope to increase the recovery factor of oil and gas fields by tailoring tools from the systems and control community to efficiently perform dynamic optimization of wells (e.g. number, locations) and their production settings (e.g. bottom-hole pressures, flow rates, valve settings) based on uncertain reservoir models, in the sense that they lead to good decisions while requiring limited time from the user. This thesis aims at developing these tools, and the main contributions are as follows. Many production setting optimization problems can be written as optimal control problems that are linear in the control. If the only constraints are upper and lower bounds on the control, these problems can be expected to have pure bang-bang optimal solutions. The adjoint method to derive gradients of a cost function with respect to production settings can be combined with robust optimization to efficiently compute settings that are robust against uncertainty in reservoir models. The gradients used in production setting optimization can be used to efficiently compute directions in which to iteratively improve upon an initial well configuration by surrounding the to-be-placed wells by pseudo wells (i.e. wells that operate at a negligible rate). The controllability and observability properties of single-phase flow reservoir model are analyzed. It is shown that pressures near wells in which we can control the flow rate or bottom-hole pressure are controllable, whereas pressures near wells in which we can measure the flow rate or bottom-hole pressure are observable. Finally, a new method of regularization in history matching is presented, based on this controllability and observability analysis.