This paper presents an innovative filtering and analysis approach to identify candidates for sidetracking in mature water flooded fields. It targets bypassed reserves to improve production and ultimate recovery from such fields at once. The method is based on production engineering concepts, it is very time efficient and requires only a minimum of data, which makes it in most cases more suitable than other methods. The approach provides a filtering concept to select all wells that might have bypassed reserves in their drainage area and provides a step by step analysis to verify, quantify, and locate these bypassed reserves. Further it provides a comprehensive method to develop a production forecast for a potential sidetrack. It also presents a set of criteria to select the most suitable well to sidetrack. Finally it allows leveraging all associated uncertainties by linking the economic analysis to a Monte Carlo simulation, which is critical for a sound management decision. Whereby, the approach tackles the four most critical factors, how much data are needed, how much resources are required, how long will it take to come to a decision, and what will be the certainty of the output. Recapitulating, the approach presented here allows selecting candidates for sidetracking in mature water flooded fields based on a minimum of data time efficiently, which allows making the right management decision to unlock some of the potential of these kind of fields at attractive ROI and NPV. Introduction The world energy demand is continuously increasing, while the number of newly explored fields is declining regardless of the effort taken and technology invested into exploration as C.J. Campbell and J.H. Laherrère1 explained. Thus the E&P companies are coming back to their brown fields re-evaluating the remaining potentials as C. Sbiti stated in an interview with the Petroleum Economist2. Today already ~70% of the world oil production is coming from brown fields3. Improving both, production and ultimate recovery from these fields is critical for growing beyond this level2. The integration of technologies and conceptual approaches, as this paper presents one, are critical in achieving this goal. Whereby, with the ever dwindling reserves base it is of the up most importance to extract all available production from existing fields, within the economic limits. In brown fields the capital investment of infrastructure are already in place. Reclaiming idling wells by sidetracking is an attractive investment option, which also allows the application of new technology into the field at low cost, when significant reserves are accessed. However, the critical aspect is if bypassed reserves remain in significant amounts to be economical given the lower investment required? This uncertainty makes unlocking the potentials of a brown field is still a reinforcing process. Which means that, as soon as potentials are proven to exist, resources will be made available to pursue them - but usually not before. Thus, identifying and proving the potentials of a field based on a minimum of data time efficiently is a key for unlocking these potentials. Target of the Approach Most of the fields in the world are water flooded. Thus, mature water flooded fields cover the highest percentage of fields and associated remaining potentials4, which makes them especially attractive. In almost any water flood, water passes by the oil on a macroscopic scale due to the heterogeneity of the reservoir. This might lead to reasonable spots of bypassed reserves, which are not accessible through any existing well. Drilling sidetracks from existing wells into these spots increases the production and improves ultimate recovery5 from the field. Achieve these two goals at once makes bypassed reserves in water flooded fields an attractive target for unlocking the potentials of such fields. Benefits of the Approach Initially, only limited resources will be available to evaluate the remaining potentials of a field. Thus, it is critical that the applied analysis approach delivers result time effectively with a minimum of data.