Assited History Matching History Matching, a fundamental process in the Reservoir Simulation workflow, is known by being a reverse engineering problem, in which a different combination of inputs could result in similarly valid outputs. Due to the uncertainty of such input data, a procedure to provide several solutions and quantitatively capture the uncertainty of predictions in order to make better decision is required. In the last decade, the so called "Uncertainty Analysis and Optimization" (or Automated History Matching; AHM), had a growing interest in the Simulation arena and virtually replaced the traditional "manual" method that provides only one possible solution. This paper presents a three-stage method: (i) Screening: Key variables and their ranges are selected, (ii) Optimization: mismatch is minimized via Evolutionary Algorithms and (iii) Maturation: multidisciplinary analysis of results and n-feedback loops to improve the Static and Dynamic model. It also presents field results of stages (i) and (ii) in a highly complex and mature field under water injection. This method, not only considerably reduced run times, but also helped to detect poorly captured key reservoir characteristics in the Static Model, becoming a resourceful tool to enhance decision making.