The paper describes an approach to selection of horizontal well placement. Given a field dynamic model we use advanced optimization techniques to select horizontal well length, well placement, well control that improve the field economics (as measured by the net present value, NPV) and increase field recoveries. Well-known problems of using optimization algorithms for field development are: (1) big number of variables, simplest well description assumes 5 variables: position, lateral length, orientation, bottom-hole pressure; (2) computational complexity of hydrodynamic simulation. In order to deal with high dimensionality and computation complexity we propose multi-layer approach. First, we decompose optimal well placement and control task of high dimension into a number of optimization problems of lower dimension: selection of optimal well pattern, local well placement optimization, selection of well control. That allows significantly decrease a number of simulation runs. Second, we use chain of simulators and dynamic models (from analytic models to fine-scale hydrodynamic model). The optimal solution for well placement obtained on simple model is used as input (baseline) solution for more complicated models. Thus we reduce number of complicated model runs. The proposed approach was implemented in experimental software that automatically optimize horizontal well placement and well control using Eclipse dynamic model. We use this software to optimize FDP of a JSC "Gazprom neft" greenfield. The optimization resulted in significant increase of the field NPV and increase of expected recovery factor: 14% and 1.7% respectively. We estimate that optimization techniques contribution is 9% of NPV and 0.4% of RF.